Overview

Dataset statistics

Number of variables50
Number of observations237805
Missing cells952069
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory92.5 MiB
Average record size in memory408.0 B

Variable types

Text4
Numeric18
Categorical21
DateTime5
Unsupported2

Alerts

Valor del Contrato is highly overall correlated with Valor Pendiente de Pago and 2 other fieldsHigh correlation
Valor de pago adelantado is highly overall correlated with Valor Pendiente de AmortizacionHigh correlation
Valor Facturado is highly overall correlated with Valor PagadoHigh correlation
Valor Pendiente de Pago is highly overall correlated with Valor del Contrato and 2 other fieldsHigh correlation
Valor Pagado is highly overall correlated with Valor Facturado and 2 other fieldsHigh correlation
Valor Pendiente de Amortizacion is highly overall correlated with Valor de pago adelantadoHigh correlation
Valor Pendiente de Ejecucion is highly overall correlated with Valor del Contrato and 2 other fieldsHigh correlation
Saldo CDP is highly overall correlated with Valor del ContratoHigh correlation
Orden is highly overall correlated with SectorHigh correlation
Sector is highly overall correlated with OrdenHigh correlation
Tipo de Contrato is highly overall correlated with Modalidad de ContratacionHigh correlation
Modalidad de Contratacion is highly overall correlated with Tipo de Contrato and 1 other fieldsHigh correlation
Es Grupo is highly overall correlated with Modalidad de ContratacionHigh correlation
EsPostConflicto is highly overall correlated with Destino GastoHigh correlation
Destino Gasto is highly overall correlated with EsPostConflictoHigh correlation
Rama is highly imbalanced (61.3%)Imbalance
Tipo de Contrato is highly imbalanced (80.3%)Imbalance
Modalidad de Contratacion is highly imbalanced (72.1%)Imbalance
Condiciones de Entrega is highly imbalanced (53.2%)Imbalance
TipoDocProveedor is highly imbalanced (79.1%)Imbalance
Es Grupo is highly imbalanced (95.0%)Imbalance
Habilita Pago Adelantado is highly imbalanced (97.4%)Imbalance
Liquidación is highly imbalanced (52.1%)Imbalance
Obligación Ambiental is highly imbalanced (83.8%)Imbalance
Obligaciones Postconsumo is highly imbalanced (99.5%)Imbalance
Reversion is highly imbalanced (99.9%)Imbalance
EsPostConflicto is highly imbalanced (94.9%)Imbalance
Fecha de Inicio de Ejecucion has 237798 (> 99.9%) missing valuesMissing
Fecha de Fin de Ejecucion has 237798 (> 99.9%) missing valuesMissing
Fecha Inicio Liquidacion has 237805 (100.0%) missing valuesMissing
Fecha Fin Liquidacion has 237805 (100.0%) missing valuesMissing
Valor del Contrato is highly skewed (γ1 = 487.5652228)Skewed
Valor de pago adelantado is highly skewed (γ1 = 268.3515114)Skewed
Valor Facturado is highly skewed (γ1 = 262.2819222)Skewed
Valor Pendiente de Pago is highly skewed (γ1 = 160.9699943)Skewed
Valor Pagado is highly skewed (γ1 = 281.631663)Skewed
Valor Amortizado is highly skewed (γ1 = 221.5055293)Skewed
Valor Pendiente de Amortizacion is highly skewed (γ1 = 269.8545923)Skewed
Valor Pendiente de Ejecucion is highly skewed (γ1 = 160.7827879)Skewed
Saldo CDP is highly skewed (γ1 = 487.4634854)Skewed
Saldo Vigencia is highly skewed (γ1 = 54.94188551)Skewed
Dias Adicionados is highly skewed (γ1 = 486.8615327)Skewed
Presupuesto General de la Nacion – PGN is highly skewed (γ1 = 225.6755301)Skewed
Sistema General de Participaciones is highly skewed (γ1 = 167.4845626)Skewed
Sistema General de Regalías is highly skewed (γ1 = 138.5649022)Skewed
Recursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas) is highly skewed (γ1 = 487.6382423)Skewed
Recursos de Credito is highly skewed (γ1 = 159.5573664)Skewed
Recursos Propios is highly skewed (γ1 = 137.3734988)Skewed
Fecha Inicio Liquidacion is an unsupported type, check if it needs cleaning or further analysisUnsupported
Fecha Fin Liquidacion is an unsupported type, check if it needs cleaning or further analysisUnsupported
Valor de pago adelantado has 237635 (99.9%) zerosZeros
Valor Facturado has 114088 (48.0%) zerosZeros
Valor Pendiente de Pago has 44489 (18.7%) zerosZeros
Valor Pagado has 144403 (60.7%) zerosZeros
Valor Amortizado has 237779 (> 99.9%) zerosZeros
Valor Pendiente de Amortizacion has 237652 (99.9%) zerosZeros
Valor Pendiente de Ejecucion has 43569 (18.3%) zerosZeros
Saldo CDP has 15160 (6.4%) zerosZeros
Saldo Vigencia has 234044 (98.4%) zerosZeros
Dias Adicionados has 203441 (85.5%) zerosZeros
Presupuesto General de la Nacion – PGN has 186497 (78.4%) zerosZeros
Sistema General de Participaciones has 226862 (95.4%) zerosZeros
Sistema General de Regalías has 235635 (99.1%) zerosZeros
Recursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas) has 116337 (48.9%) zerosZeros
Recursos de Credito has 237029 (99.7%) zerosZeros
Recursos Propios has 185428 (78.0%) zerosZeros

Reproduction

Analysis started2023-07-15 23:30:15.997334
Analysis finished2023-07-15 23:36:09.441765
Duration5 minutes and 53.44 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Distinct2604
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:09.792768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length136
Median length99
Mean length40.682782
Min length3

Characters and Unicode

Total characters9674569
Distinct characters78
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique402 ?
Unique (%)0.2%

Sample

1st rowUNIDAD EJECUTORA DE SANEAMIENTO DEL VALLE DEL CAUCA
2nd rowSecretaría de Gobierno Convivencia y Seguridad Alcaldía de Tuluá
3rd rowSECRETARIA DISTRITAL DE AMBIENTE
4th rowGOBERNACIÓN DEL DEPARTAMENTO ARCHIPIELAGO DE SAN ANDRES PROVIDENCIA Y SANTA CATALINA
5th rowALCALDIA MUNICIPAL DE MELGAR
ValueCountFrequency (%)
de 221734
 
16.5%
del 50317
 
3.8%
y 37845
 
2.8%
municipio 36631
 
2.7%
instituto 25988
 
1.9%
la 24766
 
1.8%
regional 23097
 
1.7%
alcaldia 18842
 
1.4%
distrital 17759
 
1.3%
departamento 16464
 
1.2%
Other values (2212) 868244
64.7%
2023-07-15T18:36:10.511766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1135290
11.7%
A 1040244
 
10.8%
E 874467
 
9.0%
I 854270
 
8.8%
N 548188
 
5.7%
D 546468
 
5.6%
O 546056
 
5.6%
R 530101
 
5.5%
T 473040
 
4.9%
C 472346
 
4.9%
Other values (68) 2654099
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7923124
81.9%
Space Separator 1135290
 
11.7%
Lowercase Letter 610790
 
6.3%
Decimal Number 5365
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1040244
13.1%
E 874467
11.0%
I 854270
10.8%
N 548188
 
6.9%
D 546468
 
6.9%
O 546056
 
6.9%
R 530101
 
6.7%
T 473040
 
6.0%
C 472346
 
6.0%
L 447400
 
5.6%
Other values (26) 1590544
20.1%
Lowercase Letter
ValueCountFrequency (%)
i 75784
12.4%
o 60082
9.8%
r 59732
9.8%
a 59325
9.7%
t 59238
9.7%
e 48801
 
8.0%
n 41076
 
6.7%
d 32278
 
5.3%
c 26577
 
4.4%
s 25446
 
4.2%
Other values (21) 122451
20.0%
Decimal Number
ValueCountFrequency (%)
1 3710
69.2%
2 549
 
10.2%
6 280
 
5.2%
4 244
 
4.5%
3 187
 
3.5%
8 121
 
2.3%
5 89
 
1.7%
7 72
 
1.3%
9 70
 
1.3%
0 43
 
0.8%
Space Separator
ValueCountFrequency (%)
1135290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8533914
88.2%
Common 1140655
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1040244
12.2%
E 874467
 
10.2%
I 854270
 
10.0%
N 548188
 
6.4%
D 546468
 
6.4%
O 546056
 
6.4%
R 530101
 
6.2%
T 473040
 
5.5%
C 472346
 
5.5%
L 447400
 
5.2%
Other values (57) 2201334
25.8%
Common
ValueCountFrequency (%)
1135290
99.5%
1 3710
 
0.3%
2 549
 
< 0.1%
6 280
 
< 0.1%
4 244
 
< 0.1%
3 187
 
< 0.1%
8 121
 
< 0.1%
5 89
 
< 0.1%
7 72
 
< 0.1%
9 70
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9594599
99.2%
None 79970
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1135290
11.8%
A 1040244
10.8%
E 874467
 
9.1%
I 854270
 
8.9%
N 548188
 
5.7%
D 546468
 
5.7%
O 546056
 
5.7%
R 530101
 
5.5%
T 473040
 
4.9%
C 472346
 
4.9%
Other values (51) 2574129
26.8%
None
ValueCountFrequency (%)
Ó 29663
37.1%
Í 19527
24.4%
ó 8201
 
10.3%
í 5754
 
7.2%
Ñ 5366
 
6.7%
Á 5285
 
6.6%
Ú 1806
 
2.3%
Ò 1446
 
1.8%
á 1210
 
1.5%
É 883
 
1.1%
Other values (7) 829
 
1.0%

Nit Entidad
Real number (ℝ)

Distinct2220
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6276754 × 109
Minimum90106609
Maximum9.0162899 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:10.852767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum90106609
5-th percentile8.0010393 × 108
Q18.6050617 × 108
median8.9138004 × 108
Q38.9999924 × 108
95-th percentile8.9200015 × 109
Maximum9.0162899 × 109
Range8.9261833 × 109
Interquartile range (IQR)39493069

Descriptive statistics

Standard deviation2.3246916 × 109
Coefficient of variation (CV)1.428228
Kurtosis5.633006
Mean1.6276754 × 109
Median Absolute Deviation (MAD)8619201
Skewness2.7575802
Sum3.8706934 × 1014
Variance5.4041911 × 1018
MonotonicityNot monotonic
2023-07-15T18:36:11.100763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
899999034 13475
 
5.7%
890399011 9453
 
4.0%
899999061 7871
 
3.3%
8999990619 6074
 
2.6%
899999239 3972
 
1.7%
900959048 2611
 
1.1%
900971006 2499
 
1.1%
9009585649 2179
 
0.9%
891480030 2110
 
0.9%
890480184 1994
 
0.8%
Other values (2210) 185567
78.0%
ValueCountFrequency (%)
90106609 8
 
< 0.1%
800000118 87
< 0.1%
800002916 27
 
< 0.1%
800003253 2
 
< 0.1%
800003935 118
< 0.1%
800004574 1
 
< 0.1%
800005042 4
 
< 0.1%
800005349 1
 
< 0.1%
800006095 1
 
< 0.1%
800007652 151
0.1%
ValueCountFrequency (%)
9016289940 8
 
< 0.1%
9015500212 2
 
< 0.1%
9015425882 6
 
< 0.1%
9015425732 21
 
< 0.1%
9015412568 70
< 0.1%
9015411545 81
< 0.1%
9015410214 161
0.1%
9014531831 17
 
< 0.1%
9014427611 10
 
< 0.1%
9014332637 24
 
< 0.1%

Departamento
Categorical

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Distrito Capital de Bogotá
73300 
Valle del Cauca
26378 
Antioquia
23924 
Santander
13418 
Cundinamarca
11369 
Other values (29)
89416 

Length

Max length40
Median length26
Mean length14.841841
Min length4

Characters and Unicode

Total characters3529464
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowValle del Cauca
2nd rowValle del Cauca
3rd rowDistrito Capital de Bogotá
4th rowSan Andrés, Providencia y Santa Catalina
5th rowTolima

Common Values

ValueCountFrequency (%)
Distrito Capital de Bogotá 73300
30.8%
Valle del Cauca 26378
 
11.1%
Antioquia 23924
 
10.1%
Santander 13418
 
5.6%
Cundinamarca 11369
 
4.8%
Atlántico 8201
 
3.4%
Tolima 6955
 
2.9%
Norte de Santander 6802
 
2.9%
Bolívar 6479
 
2.7%
Meta 6279
 
2.6%
Other values (24) 54700
23.0%

Length

2023-07-15T18:36:11.335781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 80102
14.9%
distrito 73300
13.6%
capital 73300
13.6%
bogotá 73300
13.6%
cauca 29888
 
5.6%
valle 26378
 
4.9%
del 26378
 
4.9%
antioquia 23924
 
4.4%
santander 20220
 
3.8%
cundinamarca 11369
 
2.1%
Other values (35) 99509
18.5%

Most occurring characters

ValueCountFrequency (%)
a 438012
12.4%
t 374039
 
10.6%
i 323723
 
9.2%
299863
 
8.5%
o 295632
 
8.4%
l 193086
 
5.5%
e 181642
 
5.1%
d 164007
 
4.6%
r 144356
 
4.1%
C 130538
 
3.7%
Other values (35) 984566
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2798413
79.3%
Uppercase Letter 428852
 
12.2%
Space Separator 299863
 
8.5%
Other Punctuation 2336
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 438012
15.7%
t 374039
13.4%
i 323723
11.6%
o 295632
10.6%
l 193086
6.9%
e 181642
 
6.5%
d 164007
 
5.9%
r 144356
 
5.2%
n 119651
 
4.3%
s 91169
 
3.3%
Other values (18) 473096
16.9%
Uppercase Letter
ValueCountFrequency (%)
C 130538
30.4%
B 85232
19.9%
D 74191
17.3%
A 35732
 
8.3%
S 27221
 
6.3%
V 26968
 
6.3%
N 11667
 
2.7%
M 9344
 
2.2%
T 6955
 
1.6%
R 5547
 
1.3%
Other values (5) 15457
 
3.6%
Space Separator
ValueCountFrequency (%)
299863
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3227265
91.4%
Common 302199
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 438012
13.6%
t 374039
11.6%
i 323723
 
10.0%
o 295632
 
9.2%
l 193086
 
6.0%
e 181642
 
5.6%
d 164007
 
5.1%
r 144356
 
4.5%
C 130538
 
4.0%
n 119651
 
3.7%
Other values (33) 862579
26.7%
Common
ValueCountFrequency (%)
299863
99.2%
, 2336
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3420301
96.9%
None 109163
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 438012
12.8%
t 374039
10.9%
i 323723
 
9.5%
299863
 
8.8%
o 295632
 
8.6%
l 193086
 
5.6%
e 181642
 
5.3%
d 164007
 
4.8%
r 144356
 
4.2%
C 130538
 
3.8%
Other values (30) 875403
25.6%
None
ValueCountFrequency (%)
á 88017
80.6%
í 11973
 
11.0%
ñ 3974
 
3.6%
ó 2800
 
2.6%
é 2399
 
2.2%

Ciudad
Text

Distinct540
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:11.923978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length27
Median length22
Mean length8.1586888
Min length3

Characters and Unicode

Total characters1940177
Distinct characters54
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)< 0.1%

Sample

1st rowCali
2nd rowTuluá
3rd rowBogotá
4th rowSan Andrés
5th rowMelgar
ValueCountFrequency (%)
no 46729
15.3%
definido 46729
15.3%
bogotá 43093
 
14.2%
cali 17943
 
5.9%
medellín 10893
 
3.6%
barranquilla 6007
 
2.0%
bucaramanga 5959
 
2.0%
cúcuta 4653
 
1.5%
villavicencio 4526
 
1.5%
san 4247
 
1.4%
Other values (554) 113705
37.3%
2023-07-15T18:36:12.788697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 231953
 
12.0%
a 214789
 
11.1%
i 174216
 
9.0%
e 127076
 
6.5%
n 119691
 
6.2%
l 95470
 
4.9%
t 77050
 
4.0%
d 76798
 
4.0%
r 73042
 
3.8%
66679
 
3.4%
Other values (44) 683413
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1569014
80.9%
Uppercase Letter 304484
 
15.7%
Space Separator 66679
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 231953
14.8%
a 214789
13.7%
i 174216
11.1%
e 127076
8.1%
n 119691
 
7.6%
l 95470
 
6.1%
t 77050
 
4.9%
d 76798
 
4.9%
r 73042
 
4.7%
g 65004
 
4.1%
Other values (20) 313925
20.0%
Uppercase Letter
ValueCountFrequency (%)
B 58392
19.2%
D 52192
17.1%
N 49266
16.2%
C 34771
11.4%
M 20782
 
6.8%
S 16769
 
5.5%
P 16532
 
5.4%
A 9634
 
3.2%
V 8144
 
2.7%
T 6858
 
2.3%
Other values (13) 31144
10.2%
Space Separator
ValueCountFrequency (%)
66679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1873498
96.6%
Common 66679
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 231953
 
12.4%
a 214789
 
11.5%
i 174216
 
9.3%
e 127076
 
6.8%
n 119691
 
6.4%
l 95470
 
5.1%
t 77050
 
4.1%
d 76798
 
4.1%
r 73042
 
3.9%
g 65004
 
3.5%
Other values (43) 618409
33.0%
Common
ValueCountFrequency (%)
66679
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1857631
95.7%
None 82546
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 231953
 
12.5%
a 214789
 
11.6%
i 174216
 
9.4%
e 127076
 
6.8%
n 119691
 
6.4%
l 95470
 
5.1%
t 77050
 
4.1%
d 76798
 
4.1%
r 73042
 
3.9%
66679
 
3.6%
Other values (37) 600867
32.3%
None
ValueCountFrequency (%)
á 51172
62.0%
í 15538
 
18.8%
é 6649
 
8.1%
ú 4832
 
5.9%
ó 3304
 
4.0%
ñ 1042
 
1.3%
ü 9
 
< 0.1%
Distinct651
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:13.188698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length65
Median length51
Mean length28.998322
Min length8

Characters and Unicode

Total characters6895946
Distinct characters55
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)< 0.1%

Sample

1st rowColombia, Valle del Cauca , Cali
2nd rowColombia, Valle del Cauca , Tuluá
3rd rowColombia, Bogotá, Bogotá
4th rowColombia, San Andrés, Providencia y Santa Catalina , San Andrés
5th rowColombia, Tolima , Melgar
ValueCountFrequency (%)
colombia 238050
25.6%
bogotá 146600
15.8%
132927
14.3%
cauca 29888
 
3.2%
del 27114
 
2.9%
valle 26397
 
2.8%
antioquia 24000
 
2.6%
santander 20233
 
2.2%
cali 17943
 
1.9%
cundinamarca 11369
 
1.2%
Other values (572) 253698
27.3%
2023-07-15T18:36:14.070921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
971527
14.1%
o 895304
13.0%
a 744128
10.8%
, 460511
 
6.7%
l 453124
 
6.6%
i 420456
 
6.1%
C 329736
 
4.8%
m 274049
 
4.0%
t 261444
 
3.8%
b 253877
 
3.7%
Other values (45) 1831790
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4704273
68.2%
Space Separator 971527
 
14.1%
Uppercase Letter 759635
 
11.0%
Other Punctuation 460511
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 895304
19.0%
a 744128
15.8%
l 453124
9.6%
i 420456
8.9%
m 274049
 
5.8%
t 261444
 
5.6%
b 253877
 
5.4%
n 191975
 
4.1%
e 188363
 
4.0%
g 171576
 
3.6%
Other values (20) 849977
18.1%
Uppercase Letter
ValueCountFrequency (%)
C 329736
43.4%
B 173831
22.9%
A 45366
 
6.0%
S 43990
 
5.8%
V 35112
 
4.6%
M 30182
 
4.0%
P 20345
 
2.7%
T 13813
 
1.8%
N 13399
 
1.8%
R 8862
 
1.2%
Other values (13) 44999
 
5.9%
Space Separator
ValueCountFrequency (%)
971527
100.0%
Other Punctuation
ValueCountFrequency (%)
, 460511
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5463908
79.2%
Common 1432038
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 895304
16.4%
a 744128
13.6%
l 453124
 
8.3%
i 420456
 
7.7%
C 329736
 
6.0%
m 274049
 
5.0%
t 261444
 
4.8%
b 253877
 
4.6%
n 191975
 
3.5%
e 188363
 
3.4%
Other values (43) 1451452
26.6%
Common
ValueCountFrequency (%)
971527
67.8%
, 460511
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6674116
96.8%
None 221830
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
971527
14.6%
o 895304
13.4%
a 744128
11.1%
, 460511
 
6.9%
l 453124
 
6.8%
i 420456
 
6.3%
C 329736
 
4.9%
m 274049
 
4.1%
t 261444
 
3.9%
b 253877
 
3.8%
Other values (38) 1609960
24.1%
None
ValueCountFrequency (%)
á 169396
76.4%
í 27425
 
12.4%
é 9048
 
4.1%
ó 6104
 
2.8%
ñ 5016
 
2.3%
ú 4832
 
2.2%
ü 9
 
< 0.1%

Orden
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Territorial
163539 
Nacional
69881 
Corporación Autónoma
 
4385

Length

Max length20
Median length11
Mean length10.28438
Min length8

Characters and Unicode

Total characters2445677
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTerritorial
2nd rowTerritorial
3rd rowTerritorial
4th rowTerritorial
5th rowTerritorial

Common Values

ValueCountFrequency (%)
Territorial 163539
68.8%
Nacional 69881
29.4%
Corporación Autónoma 4385
 
1.8%

Length

2023-07-15T18:36:14.275918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:14.472900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
territorial 163539
67.5%
nacional 69881
28.9%
corporación 4385
 
1.8%
autónoma 4385
 
1.8%

Most occurring characters

ValueCountFrequency (%)
r 499387
20.4%
i 401344
16.4%
a 312071
12.8%
o 246575
10.1%
l 233420
9.5%
t 167924
 
6.9%
T 163539
 
6.7%
e 163539
 
6.7%
n 78651
 
3.2%
c 74266
 
3.0%
Other values (8) 104961
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2199102
89.9%
Uppercase Letter 242190
 
9.9%
Space Separator 4385
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 499387
22.7%
i 401344
18.3%
a 312071
14.2%
o 246575
11.2%
l 233420
10.6%
t 167924
 
7.6%
e 163539
 
7.4%
n 78651
 
3.6%
c 74266
 
3.4%
ó 8770
 
0.4%
Other values (3) 13155
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
T 163539
67.5%
N 69881
28.9%
C 4385
 
1.8%
A 4385
 
1.8%
Space Separator
ValueCountFrequency (%)
4385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2441292
99.8%
Common 4385
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 499387
20.5%
i 401344
16.4%
a 312071
12.8%
o 246575
10.1%
l 233420
9.6%
t 167924
 
6.9%
T 163539
 
6.7%
e 163539
 
6.7%
n 78651
 
3.2%
c 74266
 
3.0%
Other values (7) 100576
 
4.1%
Common
ValueCountFrequency (%)
4385
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2436907
99.6%
None 8770
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 499387
20.5%
i 401344
16.5%
a 312071
12.8%
o 246575
10.1%
l 233420
9.6%
t 167924
 
6.9%
T 163539
 
6.7%
e 163539
 
6.7%
n 78651
 
3.2%
c 74266
 
3.0%
Other values (7) 96191
 
3.9%
None
ValueCountFrequency (%)
ó 8770
100.0%

Sector
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Servicio Público
63031 
No aplica/No pertenece
39072 
Salud y Protección Social
28874 
Educación Nacional
13637 
Trabajo
12138 
Other values (20)
81053 

Length

Max length50
Median length33
Mean length18.251668
Min length7

Characters and Unicode

Total characters4340338
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalud y Protección Social
2nd rowServicio Público
3rd rowAmbiente y Desarrollo Sostenible
4th rowNo aplica/No pertenece
5th rowNo aplica/No pertenece

Common Values

ValueCountFrequency (%)
Servicio Público 63031
26.5%
No aplica/No pertenece 39072
16.4%
Salud y Protección Social 28874
12.1%
Educación Nacional 13637
 
5.7%
Trabajo 12138
 
5.1%
Ambiente y Desarrollo Sostenible 11531
 
4.8%
deportes 11057
 
4.6%
defensa 8477
 
3.6%
Cultura 7348
 
3.1%
Inclusión Social y Reconciliación 7103
 
3.0%
Other values (15) 35537
14.9%

Length

2023-07-15T18:36:14.648902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
público 66190
 
11.4%
servicio 63031
 
10.8%
y 57930
 
10.0%
no 39072
 
6.7%
aplica/no 39072
 
6.7%
pertenece 39072
 
6.7%
social 35977
 
6.2%
salud 28874
 
5.0%
protección 28874
 
5.0%
nacional 13637
 
2.3%
Other values (40) 169962
29.2%

Most occurring characters

ValueCountFrequency (%)
i 437435
 
10.1%
c 396521
 
9.1%
e 392953
 
9.1%
o 381176
 
8.8%
343886
 
7.9%
a 304418
 
7.0%
l 252275
 
5.8%
r 233770
 
5.4%
n 195499
 
4.5%
t 140519
 
3.2%
Other values (33) 1261886
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3501472
80.7%
Uppercase Letter 451322
 
10.4%
Space Separator 343886
 
7.9%
Other Punctuation 43658
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 437435
12.5%
c 396521
11.3%
e 392953
11.2%
o 381176
10.9%
a 304418
8.7%
l 252275
 
7.2%
r 233770
 
6.7%
n 195499
 
5.6%
t 140519
 
4.0%
b 102109
 
2.9%
Other values (15) 664797
19.0%
Uppercase Letter
ValueCountFrequency (%)
S 139413
30.9%
P 98699
21.9%
N 91781
20.3%
T 25304
 
5.6%
E 17730
 
3.9%
C 16899
 
3.7%
I 13484
 
3.0%
A 11531
 
2.6%
D 11531
 
2.6%
R 8001
 
1.8%
Other values (5) 16949
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 39072
89.5%
, 4586
 
10.5%
Space Separator
ValueCountFrequency (%)
343886
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3952794
91.1%
Common 387544
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 437435
11.1%
c 396521
 
10.0%
e 392953
 
9.9%
o 381176
 
9.6%
a 304418
 
7.7%
l 252275
 
6.4%
r 233770
 
5.9%
n 195499
 
4.9%
t 140519
 
3.6%
S 139413
 
3.5%
Other values (30) 1078815
27.3%
Common
ValueCountFrequency (%)
343886
88.7%
/ 39072
 
10.1%
, 4586
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4201265
96.8%
None 139073
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 437435
 
10.4%
c 396521
 
9.4%
e 392953
 
9.4%
o 381176
 
9.1%
343886
 
8.2%
a 304418
 
7.2%
l 252275
 
6.0%
r 233770
 
5.6%
n 195499
 
4.7%
t 140519
 
3.3%
Other values (29) 1122813
26.7%
None
ValueCountFrequency (%)
ú 66909
48.1%
ó 63290
45.5%
í 5710
 
4.1%
é 3164
 
2.3%

Rama
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Ejecutivo
194358 
Corporación Autónoma
39959 
Legislativo
 
1956
Judicial
 
1532

Length

Max length20
Median length9
Mean length10.858367
Min length8

Characters and Unicode

Total characters2582174
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEjecutivo
2nd rowEjecutivo
3rd rowEjecutivo
4th rowEjecutivo
5th rowEjecutivo

Common Values

ValueCountFrequency (%)
Ejecutivo 194358
81.7%
Corporación Autónoma 39959
 
16.8%
Legislativo 1956
 
0.8%
Judicial 1532
 
0.6%

Length

2023-07-15T18:36:14.889905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:15.094952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ejecutivo 194358
70.0%
corporación 39959
 
14.4%
autónoma 39959
 
14.4%
legislativo 1956
 
0.7%
judicial 1532
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 316191
12.2%
i 241293
9.3%
t 236273
9.2%
c 235849
9.1%
u 235849
9.1%
e 196314
7.6%
v 196314
7.6%
E 194358
7.5%
j 194358
7.5%
a 83406
 
3.2%
Other values (14) 451969
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2264451
87.7%
Uppercase Letter 277764
 
10.8%
Space Separator 39959
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 316191
14.0%
i 241293
10.7%
t 236273
10.4%
c 235849
10.4%
u 235849
10.4%
e 196314
8.7%
v 196314
8.7%
j 194358
8.6%
a 83406
 
3.7%
r 79918
 
3.5%
Other values (8) 248686
11.0%
Uppercase Letter
ValueCountFrequency (%)
E 194358
70.0%
C 39959
 
14.4%
A 39959
 
14.4%
L 1956
 
0.7%
J 1532
 
0.6%
Space Separator
ValueCountFrequency (%)
39959
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2542215
98.5%
Common 39959
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 316191
12.4%
i 241293
9.5%
t 236273
9.3%
c 235849
9.3%
u 235849
9.3%
e 196314
7.7%
v 196314
7.7%
E 194358
7.6%
j 194358
7.6%
a 83406
 
3.3%
Other values (13) 412010
16.2%
Common
ValueCountFrequency (%)
39959
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2502256
96.9%
None 79918
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 316191
12.6%
i 241293
9.6%
t 236273
9.4%
c 235849
9.4%
u 235849
9.4%
e 196314
7.8%
v 196314
7.8%
E 194358
7.8%
j 194358
7.8%
a 83406
 
3.3%
Other values (13) 372051
14.9%
None
ValueCountFrequency (%)
ó 79918
100.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Descentralizada
128917 
Centralizada
99751 
No Definido
 
9137

Length

Max length15
Median length15
Mean length13.587914
Min length11

Characters and Unicode

Total characters3231274
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentralizada
2nd rowDescentralizada
3rd rowCentralizada
4th rowDescentralizada
5th rowCentralizada

Common Values

ValueCountFrequency (%)
Descentralizada 128917
54.2%
Centralizada 99751
41.9%
No Definido 9137
 
3.8%

Length

2023-07-15T18:36:15.321234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:15.562779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
descentralizada 128917
52.2%
centralizada 99751
40.4%
no 9137
 
3.7%
definido 9137
 
3.7%

Most occurring characters

ValueCountFrequency (%)
a 686004
21.2%
e 366722
11.3%
i 246942
 
7.6%
d 237805
 
7.4%
n 237805
 
7.4%
l 228668
 
7.1%
z 228668
 
7.1%
t 228668
 
7.1%
r 228668
 
7.1%
D 138054
 
4.3%
Other values (7) 403270
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2975195
92.1%
Uppercase Letter 246942
 
7.6%
Space Separator 9137
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 686004
23.1%
e 366722
12.3%
i 246942
 
8.3%
d 237805
 
8.0%
n 237805
 
8.0%
l 228668
 
7.7%
z 228668
 
7.7%
t 228668
 
7.7%
r 228668
 
7.7%
c 128917
 
4.3%
Other values (3) 156328
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
D 138054
55.9%
C 99751
40.4%
N 9137
 
3.7%
Space Separator
ValueCountFrequency (%)
9137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3222137
99.7%
Common 9137
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 686004
21.3%
e 366722
11.4%
i 246942
 
7.7%
d 237805
 
7.4%
n 237805
 
7.4%
l 228668
 
7.1%
z 228668
 
7.1%
t 228668
 
7.1%
r 228668
 
7.1%
D 138054
 
4.3%
Other values (6) 394133
12.2%
Common
ValueCountFrequency (%)
9137
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3231274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 686004
21.2%
e 366722
11.3%
i 246942
 
7.6%
d 237805
 
7.4%
n 237805
 
7.4%
l 228668
 
7.1%
z 228668
 
7.1%
t 228668
 
7.1%
r 228668
 
7.1%
D 138054
 
4.3%
Other values (7) 403270
12.5%

Estado Contrato
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
En ejecución
141657 
Modificado
53562 
terminado
26724 
Cerrado
 
13148
cedido
 
2211
Other values (3)
 
503

Length

Max length12
Median length12
Mean length10.874851
Min length6

Characters and Unicode

Total characters2586094
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowEn ejecución
2nd rowEn ejecución
3rd rowCerrado
4th rowEn ejecución
5th rowEn ejecución

Common Values

ValueCountFrequency (%)
En ejecución 141657
59.6%
Modificado 53562
 
22.5%
terminado 26724
 
11.2%
Cerrado 13148
 
5.5%
cedido 2211
 
0.9%
Suspendido 438
 
0.2%
Activo 64
 
< 0.1%
Borrador 1
 
< 0.1%

Length

2023-07-15T18:36:15.810778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:16.063780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
en 141657
37.3%
ejecución 141657
37.3%
modificado 53562
 
14.1%
terminado 26724
 
7.0%
cerrado 13148
 
3.5%
cedido 2211
 
0.6%
suspendido 438
 
0.1%
activo 64
 
< 0.1%
borrador 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 339151
13.1%
e 325835
12.6%
n 310476
12.0%
i 278218
10.8%
d 152295
 
5.9%
o 149711
 
5.8%
u 142095
 
5.5%
E 141657
 
5.5%
141657
 
5.5%
j 141657
 
5.5%
Other values (14) 463342
17.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2235567
86.4%
Uppercase Letter 208870
 
8.1%
Space Separator 141657
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 339151
15.2%
e 325835
14.6%
n 310476
13.9%
i 278218
12.4%
d 152295
6.8%
o 149711
6.7%
u 142095
6.4%
j 141657
6.3%
ó 141657
6.3%
a 93435
 
4.2%
Other values (7) 161037
7.2%
Uppercase Letter
ValueCountFrequency (%)
E 141657
67.8%
M 53562
 
25.6%
C 13148
 
6.3%
S 438
 
0.2%
A 64
 
< 0.1%
B 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
141657
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2444437
94.5%
Common 141657
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 339151
13.9%
e 325835
13.3%
n 310476
12.7%
i 278218
11.4%
d 152295
6.2%
o 149711
6.1%
u 142095
5.8%
E 141657
5.8%
j 141657
5.8%
ó 141657
5.8%
Other values (13) 321685
13.2%
Common
ValueCountFrequency (%)
141657
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2444437
94.5%
None 141657
 
5.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 339151
13.9%
e 325835
13.3%
n 310476
12.7%
i 278218
11.4%
d 152295
6.2%
o 149711
6.1%
u 142095
5.8%
E 141657
5.8%
141657
5.8%
j 141657
5.8%
Other values (13) 321685
13.2%
None
ValueCountFrequency (%)
ó 141657
100.0%

Tipo de Contrato
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Prestación de servicios
209106 
Otro
 
7095
DecreeLaw092/2017
 
7046
Suministros
 
4839
Compraventa
 
3189
Other values (17)
 
6530

Length

Max length26
Median length23
Mean length21.62066
Min length4

Characters and Unicode

Total characters5141501
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrestación de servicios
2nd rowPrestación de servicios
3rd rowPrestación de servicios
4th rowPrestación de servicios
5th rowPrestación de servicios

Common Values

ValueCountFrequency (%)
Prestación de servicios 209106
87.9%
Otro 7095
 
3.0%
DecreeLaw092/2017 7046
 
3.0%
Suministros 4839
 
2.0%
Compraventa 3189
 
1.3%
Arrendamiento de inmuebles 2078
 
0.9%
Obra 1652
 
0.7%
No Especificado 892
 
0.4%
Interventoría 466
 
0.2%
Comodato 435
 
0.2%
Other values (12) 1007
 
0.4%

Length

2023-07-15T18:36:16.351790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 211314
31.9%
servicios 209158
31.6%
prestación 209106
31.6%
otro 7095
 
1.1%
decreelaw092/2017 7046
 
1.1%
suministros 4839
 
0.7%
compraventa 3189
 
0.5%
arrendamiento 2169
 
0.3%
inmuebles 2080
 
0.3%
obra 1652
 
0.2%
Other values (20) 3811
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 664988
12.9%
i 643404
12.5%
s 641014
12.5%
r 448317
8.7%
c 427311
8.3%
423654
8.2%
o 231402
 
4.5%
a 228677
 
4.4%
t 228241
 
4.4%
n 225024
 
4.4%
Other values (31) 979469
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4415644
85.9%
Space Separator 423654
 
8.2%
Uppercase Letter 245835
 
4.8%
Decimal Number 49322
 
1.0%
Other Punctuation 7046
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 664988
15.1%
i 643404
14.6%
s 641014
14.5%
r 448317
10.2%
c 427311
9.7%
o 231402
 
5.2%
a 228677
 
5.2%
t 228241
 
5.2%
n 225024
 
5.1%
d 214867
 
4.9%
Other values (12) 462399
10.5%
Uppercase Letter
ValueCountFrequency (%)
P 209159
85.1%
O 8747
 
3.6%
L 7046
 
2.9%
D 7046
 
2.9%
S 5241
 
2.1%
C 4030
 
1.6%
A 2215
 
0.9%
E 925
 
0.4%
N 903
 
0.4%
I 466
 
0.2%
Other values (2) 57
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 14092
28.6%
2 14092
28.6%
9 7046
14.3%
1 7046
14.3%
7 7046
14.3%
Space Separator
ValueCountFrequency (%)
423654
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 7046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4661479
90.7%
Common 480022
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 664988
14.3%
i 643404
13.8%
s 641014
13.8%
r 448317
9.6%
c 427311
9.2%
o 231402
 
5.0%
a 228677
 
4.9%
t 228241
 
4.9%
n 225024
 
4.8%
d 214867
 
4.6%
Other values (24) 708234
15.2%
Common
ValueCountFrequency (%)
423654
88.3%
0 14092
 
2.9%
2 14092
 
2.9%
9 7046
 
1.5%
/ 7046
 
1.5%
1 7046
 
1.5%
7 7046
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4931509
95.9%
None 209992
 
4.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 664988
13.5%
i 643404
13.0%
s 641014
13.0%
r 448317
9.1%
c 427311
8.7%
423654
8.6%
o 231402
 
4.7%
a 228677
 
4.6%
t 228241
 
4.6%
n 225024
 
4.6%
Other values (28) 769477
15.6%
None
ValueCountFrequency (%)
ó 209127
99.6%
í 858
 
0.4%
ú 7
 
< 0.1%

Modalidad de Contratacion
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Contratación directa
190899 
Contratación régimen especial
26602 
Mínima cuantía
 
10422
Contratación Directa (con ofertas)
 
2157
Selección Abreviada de Menor Cuantía
 
2012
Other values (11)
 
5713

Length

Max length59
Median length20
Mean length21.351725
Min length11

Characters and Unicode

Total characters5077547
Distinct characters42
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowContratación directa
2nd rowContratación directa
3rd rowContratación directa
4th rowContratación directa
5th rowContratación directa

Common Values

ValueCountFrequency (%)
Contratación directa 190899
80.3%
Contratación régimen especial 26602
 
11.2%
Mínima cuantía 10422
 
4.4%
Contratación Directa (con ofertas) 2157
 
0.9%
Selección Abreviada de Menor Cuantía 2012
 
0.8%
Contratación régimen especial (con ofertas) 1829
 
0.8%
Selección abreviada subasta inversa 1544
 
0.6%
No Definido 670
 
0.3%
CCE-20-Concurso_Meritos_Sin_Lista_Corta_1Sobre 601
 
0.3%
Licitación pública 545
 
0.2%
Other values (6) 524
 
0.2%

Length

2023-07-15T18:36:16.544806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
contratación 221487
42.4%
directa 193056
37.0%
régimen 28431
 
5.4%
especial 28431
 
5.4%
cuantía 12434
 
2.4%
mínima 10422
 
2.0%
con 4004
 
0.8%
ofertas 3986
 
0.8%
abreviada 3633
 
0.7%
selección 3556
 
0.7%
Other values (26) 12482
 
2.4%

Most occurring characters

ValueCountFrequency (%)
a 721478
14.2%
t 657022
12.9%
n 508834
10.0%
i 498410
9.8%
c 468763
9.2%
r 457273
9.0%
e 301219
5.9%
284117
 
5.6%
o 236256
 
4.7%
ó 226022
 
4.5%
Other values (32) 718153
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4526600
89.1%
Space Separator 284117
 
5.6%
Uppercase Letter 252848
 
5.0%
Open Punctuation 3986
 
0.1%
Close Punctuation 3986
 
0.1%
Connector Punctuation 3005
 
0.1%
Decimal Number 1803
 
< 0.1%
Dash Punctuation 1202
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 721478
15.9%
t 657022
14.5%
n 508834
11.2%
i 498410
11.0%
c 468763
10.4%
r 457273
10.1%
e 301219
6.7%
o 236256
 
5.2%
ó 226022
 
5.0%
d 197334
 
4.4%
Other values (13) 253989
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
C 225993
89.4%
M 13228
 
5.2%
S 4912
 
1.9%
D 2827
 
1.1%
A 2128
 
0.8%
L 1562
 
0.6%
N 670
 
0.3%
E 619
 
0.2%
P 455
 
0.2%
O 377
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 601
33.3%
0 601
33.3%
1 601
33.3%
Space Separator
ValueCountFrequency (%)
284117
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3986
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3986
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3005
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4779448
94.1%
Common 298099
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 721478
15.1%
t 657022
13.7%
n 508834
10.6%
i 498410
10.4%
c 468763
9.8%
r 457273
9.6%
e 301219
6.3%
o 236256
 
4.9%
ó 226022
 
4.7%
C 225993
 
4.7%
Other values (24) 478178
10.0%
Common
ValueCountFrequency (%)
284117
95.3%
( 3986
 
1.3%
) 3986
 
1.3%
_ 3005
 
1.0%
- 1202
 
0.4%
2 601
 
0.2%
0 601
 
0.2%
1 601
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4799264
94.5%
None 278283
 
5.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 721478
15.0%
t 657022
13.7%
n 508834
10.6%
i 498410
10.4%
c 468763
9.8%
r 457273
9.5%
e 301219
6.3%
284117
 
5.9%
o 236256
 
4.9%
C 225993
 
4.7%
Other values (28) 439899
9.2%
None
ValueCountFrequency (%)
ó 226022
81.2%
é 28444
 
10.2%
í 22856
 
8.2%
ú 961
 
0.3%
Distinct683
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size3.6 MiB
Minimum2020-06-04 00:00:00
Maximum2023-07-10 00:00:00
2023-07-15T18:36:16.780826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:17.028828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct599
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Minimum2022-01-01 00:00:00
Maximum2025-12-05 00:00:00
2023-07-15T18:36:17.324836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:17.567908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1392
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Memory size3.6 MiB
Minimum2021-06-30 00:00:00
Maximum2051-01-27 00:00:00
2023-07-15T18:36:17.793057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:18.017075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct7
Distinct (%)100.0%
Missing237798
Missing (%)> 99.9%
Memory size3.6 MiB
Minimum2022-02-28 00:00:00
Maximum2023-05-31 00:00:00
2023-07-15T18:36:18.196076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:18.351076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
Distinct3
Distinct (%)42.9%
Missing237798
Missing (%)> 99.9%
Memory size3.6 MiB
Minimum2023-04-24 00:00:00
Maximum2023-08-31 00:00:00
2023-07-15T18:36:18.531080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:18.703079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
A convenir
98672 
Como acordado previamente
67262 
No Definido
64684 
Transporte incluido
 
6012
Transporte no incluído
 
868
Other values (8)
 
307

Length

Max length58
Median length56
Mean length14.829041
Min length10

Characters and Unicode

Total characters3526420
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA convenir
2nd rowA convenir
3rd rowA convenir
4th rowA convenir
5th rowComo acordado previamente

Common Values

ValueCountFrequency (%)
A convenir 98672
41.5%
Como acordado previamente 67262
28.3%
No Definido 64684
27.2%
Transporte incluido 6012
 
2.5%
Transporte no incluído 868
 
0.4%
DAP - Entregado en un punto (lugar de destino convenido) 138
 
0.1%
Transporte a cargo del comprador 133
 
0.1%
CPT - Transporte pagado hasta (lugar de destino convenido) 13
 
< 0.1%
CFR - Cost and freight 12
 
< 0.1%
DDP - Delivery duty place 4
 
< 0.1%
Other values (3) 7
 
< 0.1%

Length

2023-07-15T18:36:18.899072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 98805
18.1%
convenir 98672
18.1%
como 67262
12.3%
acordado 67262
12.3%
previamente 67262
12.3%
no 65552
12.0%
definido 64684
11.9%
transporte 7026
 
1.3%
incluido 6012
 
1.1%
incluído 868
 
0.2%
Other values (34) 2002
 
0.4%

Most occurring characters

ValueCountFrequency (%)
o 513019
14.5%
e 373069
10.6%
n 345093
9.8%
i 308527
8.7%
307602
8.7%
r 247968
 
7.0%
a 209589
 
5.9%
d 206980
 
5.9%
c 173238
 
4.9%
v 166092
 
4.7%
Other values (28) 675243
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2915329
82.7%
Space Separator 307602
 
8.7%
Uppercase Letter 303012
 
8.6%
Dash Punctuation 175
 
< 0.1%
Open Punctuation 151
 
< 0.1%
Close Punctuation 151
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 513019
17.6%
e 373069
12.8%
n 345093
11.8%
i 308527
10.6%
r 247968
8.5%
a 209589
7.2%
d 206980
7.1%
c 173238
 
5.9%
v 166092
 
5.7%
m 134660
 
4.6%
Other values (12) 237094
8.1%
Uppercase Letter
ValueCountFrequency (%)
A 98813
32.6%
C 67305
22.2%
D 64840
21.4%
N 64684
21.3%
T 7042
 
2.3%
P 158
 
0.1%
E 140
 
< 0.1%
F 12
 
< 0.1%
R 12
 
< 0.1%
I 3
 
< 0.1%
Other values (2) 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
307602
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175
100.0%
Open Punctuation
ValueCountFrequency (%)
( 151
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3218341
91.3%
Common 308079
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 513019
15.9%
e 373069
11.6%
n 345093
10.7%
i 308527
9.6%
r 247968
7.7%
a 209589
 
6.5%
d 206980
 
6.4%
c 173238
 
5.4%
v 166092
 
5.2%
m 134660
 
4.2%
Other values (24) 540106
16.8%
Common
ValueCountFrequency (%)
307602
99.8%
- 175
 
0.1%
( 151
 
< 0.1%
) 151
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3525552
> 99.9%
None 868
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 513019
14.6%
e 373069
10.6%
n 345093
9.8%
i 308527
8.8%
307602
8.7%
r 247968
 
7.0%
a 209589
 
5.9%
d 206980
 
5.9%
c 173238
 
4.9%
v 166092
 
4.7%
Other values (27) 674375
19.1%
None
ValueCountFrequency (%)
í 868
100.0%

TipoDocProveedor
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Cédula de Ciudadanía
204388 
No Definido
32099 
Sin Descripcion
 
791
Cédula de Extranjería
 
430
Tarjeta de Identidad
 
45
Other values (3)
 
52

Length

Max length31
Median length20
Mean length18.769891
Min length9

Characters and Unicode

Total characters4463574
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCédula de Ciudadanía
2nd rowCédula de Ciudadanía
3rd rowCédula de Ciudadanía
4th rowCédula de Ciudadanía
5th rowCédula de Ciudadanía

Common Values

ValueCountFrequency (%)
Cédula de Ciudadanía 204388
85.9%
No Definido 32099
 
13.5%
Sin Descripcion 791
 
0.3%
Cédula de Extranjería 430
 
0.2%
Tarjeta de Identidad 45
 
< 0.1%
Pasaporte 31
 
< 0.1%
Permiso por Protección Temporal 11
 
< 0.1%
Permiso especial de permanencia 10
 
< 0.1%

Length

2023-07-15T18:36:19.075084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:19.293074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
de 204873
30.1%
cédula 204818
30.1%
ciudadanía 204388
30.0%
no 32099
 
4.7%
definido 32099
 
4.7%
sin 791
 
0.1%
descripcion 791
 
0.1%
extranjería 430
 
0.1%
identidad 45
 
< 0.1%
tarjeta 45
 
< 0.1%
Other values (7) 105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d 850701
19.1%
a 819080
18.4%
442679
9.9%
C 409206
9.2%
u 409206
9.2%
i 271056
 
6.1%
n 238575
 
5.3%
e 238397
 
5.3%
l 204839
 
4.6%
í 204818
 
4.6%
Other values (19) 375017
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3545315
79.4%
Uppercase Letter 475580
 
10.7%
Space Separator 442679
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 850701
24.0%
a 819080
23.1%
u 409206
11.5%
i 271056
 
7.6%
n 238575
 
6.7%
e 238397
 
6.7%
l 204839
 
5.8%
í 204818
 
5.8%
é 204818
 
5.8%
o 65074
 
1.8%
Other values (10) 38751
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
C 409206
86.0%
D 32890
 
6.9%
N 32099
 
6.7%
S 791
 
0.2%
E 430
 
0.1%
P 63
 
< 0.1%
T 56
 
< 0.1%
I 45
 
< 0.1%
Space Separator
ValueCountFrequency (%)
442679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4020895
90.1%
Common 442679
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 850701
21.2%
a 819080
20.4%
C 409206
10.2%
u 409206
10.2%
i 271056
 
6.7%
n 238575
 
5.9%
e 238397
 
5.9%
l 204839
 
5.1%
í 204818
 
5.1%
é 204818
 
5.1%
Other values (18) 170199
 
4.2%
Common
ValueCountFrequency (%)
442679
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4053927
90.8%
None 409647
 
9.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 850701
21.0%
a 819080
20.2%
442679
10.9%
C 409206
10.1%
u 409206
10.1%
i 271056
 
6.7%
n 238575
 
5.9%
e 238397
 
5.9%
l 204839
 
5.1%
o 65074
 
1.6%
Other values (16) 105114
 
2.6%
None
ValueCountFrequency (%)
í 204818
50.0%
é 204818
50.0%
ó 11
 
< 0.1%

Es Grupo
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
No
236458 
Si
 
1347

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters475610
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 236458
99.4%
Si 1347
 
0.6%

Length

2023-07-15T18:36:19.518079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:19.687077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 236458
99.4%
si 1347
 
0.6%

Most occurring characters

ValueCountFrequency (%)
N 236458
49.7%
o 236458
49.7%
S 1347
 
0.3%
i 1347
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 237805
50.0%
Lowercase Letter 237805
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 236458
99.4%
S 1347
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
o 236458
99.4%
i 1347
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 475610
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 236458
49.7%
o 236458
49.7%
S 1347
 
0.3%
i 1347
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 236458
49.7%
o 236458
49.7%
S 1347
 
0.3%
i 1347
 
0.3%

Es Pyme
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
No
205615 
Si
32190 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters475610
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 205615
86.5%
Si 32190
 
13.5%

Length

2023-07-15T18:36:19.857074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:20.050074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 205615
86.5%
si 32190
 
13.5%

Most occurring characters

ValueCountFrequency (%)
N 205615
43.2%
o 205615
43.2%
S 32190
 
6.8%
i 32190
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 237805
50.0%
Lowercase Letter 237805
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 205615
86.5%
S 32190
 
13.5%
Lowercase Letter
ValueCountFrequency (%)
o 205615
86.5%
i 32190
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 475610
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 205615
43.2%
o 205615
43.2%
S 32190
 
6.8%
i 32190
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 205615
43.2%
o 205615
43.2%
S 32190
 
6.8%
i 32190
 
6.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
No
236815 
No Definido
 
850
Si
 
140

Length

Max length11
Median length2
Mean length2.0321692
Min length2

Characters and Unicode

Total characters483260
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 236815
99.6%
No Definido 850
 
0.4%
Si 140
 
0.1%

Length

2023-07-15T18:36:20.221076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:20.378093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 237665
99.6%
definido 850
 
0.4%
si 140
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 238515
49.4%
N 237665
49.2%
i 1840
 
0.4%
850
 
0.2%
D 850
 
0.2%
e 850
 
0.2%
f 850
 
0.2%
n 850
 
0.2%
d 850
 
0.2%
S 140
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 243755
50.4%
Uppercase Letter 238655
49.4%
Space Separator 850
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 238515
97.9%
i 1840
 
0.8%
e 850
 
0.3%
f 850
 
0.3%
n 850
 
0.3%
d 850
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 237665
99.6%
D 850
 
0.4%
S 140
 
0.1%
Space Separator
ValueCountFrequency (%)
850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 482410
99.8%
Common 850
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 238515
49.4%
N 237665
49.3%
i 1840
 
0.4%
D 850
 
0.2%
e 850
 
0.2%
f 850
 
0.2%
n 850
 
0.2%
d 850
 
0.2%
S 140
 
< 0.1%
Common
ValueCountFrequency (%)
850
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 238515
49.4%
N 237665
49.2%
i 1840
 
0.4%
850
 
0.2%
D 850
 
0.2%
e 850
 
0.2%
f 850
 
0.2%
n 850
 
0.2%
d 850
 
0.2%
S 140
 
< 0.1%

Liquidación
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
No
213291 
Si
24514 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters475610
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 213291
89.7%
Si 24514
 
10.3%

Length

2023-07-15T18:36:20.557076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:20.778079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 213291
89.7%
si 24514
 
10.3%

Most occurring characters

ValueCountFrequency (%)
N 213291
44.8%
o 213291
44.8%
S 24514
 
5.2%
i 24514
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 237805
50.0%
Lowercase Letter 237805
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 213291
89.7%
S 24514
 
10.3%
Lowercase Letter
ValueCountFrequency (%)
o 213291
89.7%
i 24514
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 475610
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 213291
44.8%
o 213291
44.8%
S 24514
 
5.2%
i 24514
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 213291
44.8%
o 213291
44.8%
S 24514
 
5.2%
i 24514
 
5.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
No
232171 
Si
 
5634

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters475610
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 232171
97.6%
Si 5634
 
2.4%

Length

2023-07-15T18:36:20.970076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:21.183075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 232171
97.6%
si 5634
 
2.4%

Most occurring characters

ValueCountFrequency (%)
N 232171
48.8%
o 232171
48.8%
S 5634
 
1.2%
i 5634
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 237805
50.0%
Lowercase Letter 237805
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 232171
97.6%
S 5634
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
o 232171
97.6%
i 5634
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 475610
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 232171
48.8%
o 232171
48.8%
S 5634
 
1.2%
i 5634
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 232171
48.8%
o 232171
48.8%
S 5634
 
1.2%
i 5634
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
No
237712 
Si
 
93

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters475610
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 237712
> 99.9%
Si 93
 
< 0.1%

Length

2023-07-15T18:36:21.354075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:21.517072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 237712
> 99.9%
si 93
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 237712
50.0%
o 237712
50.0%
S 93
 
< 0.1%
i 93
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 237805
50.0%
Lowercase Letter 237805
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 237712
> 99.9%
S 93
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
o 237712
> 99.9%
i 93
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 475610
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 237712
50.0%
o 237712
50.0%
S 93
 
< 0.1%
i 93
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 237712
50.0%
o 237712
50.0%
S 93
 
< 0.1%
i 93
 
< 0.1%

Reversion
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
No
237790 
Si
 
15

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters475610
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 237790
> 99.9%
Si 15
 
< 0.1%

Length

2023-07-15T18:36:21.649076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:21.804091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 237790
> 99.9%
si 15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 237790
50.0%
o 237790
50.0%
S 15
 
< 0.1%
i 15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 237805
50.0%
Lowercase Letter 237805
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 237790
> 99.9%
S 15
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
o 237790
> 99.9%
i 15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 475610
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 237790
50.0%
o 237790
50.0%
S 15
 
< 0.1%
i 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 237790
50.0%
o 237790
50.0%
S 15
 
< 0.1%
i 15
 
< 0.1%

Valor del Contrato
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct80967
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2800331 × 108
Minimum0
Maximum1.4207686 × 1014
Zeros1279
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:21.959075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3697813.4
Q110333333
median19452066
Q337986667
95-th percentile1.4610228 × 108
Maximum1.4207686 × 1014
Range1.4207686 × 1014
Interquartile range (IQR)27653334

Descriptive statistics

Standard deviation2.9136572 × 1011
Coefficient of variation (CV)400.22582
Kurtosis237748.1
Mean7.2800331 × 108
Median Absolute Deviation (MAD)11052066
Skewness487.56522
Sum1.7312283 × 1014
Variance8.4893984 × 1022
MonotonicityNot monotonic
2023-07-15T18:36:22.189075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000000 2410
 
1.0%
18000000 1876
 
0.8%
9000000 1839
 
0.8%
10000000 1622
 
0.7%
15000000 1582
 
0.7%
6000000 1560
 
0.7%
24000000 1406
 
0.6%
8000000 1377
 
0.6%
14000000 1362
 
0.6%
21000000 1303
 
0.5%
Other values (80957) 221468
93.1%
ValueCountFrequency (%)
0 1279
0.5%
1 23
 
< 0.1%
3000 1
 
< 0.1%
3703 1
 
< 0.1%
10000 1
 
< 0.1%
11090 1
 
< 0.1%
16824 1
 
< 0.1%
18000 1
 
< 0.1%
18674 1
 
< 0.1%
22262 1
 
< 0.1%
ValueCountFrequency (%)
1.420768642 × 10141
< 0.1%
8.61222696 × 10111
< 0.1%
6.300077483 × 10111
< 0.1%
4.547759218 × 10111
< 0.1%
3.448830208 × 10111
< 0.1%
3.19366761 × 10111
< 0.1%
3.190473682 × 10111
< 0.1%
2.449598007 × 10111
< 0.1%
2.231804056 × 10111
< 0.1%
2.202424716 × 10111
< 0.1%

Valor de pago adelantado
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct165
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284200.87
Minimum0
Maximum1.3805894 × 1010
Zeros237635
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:22.466075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.3805894 × 1010
Range1.3805894 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38403317
Coefficient of variation (CV)135.12737
Kurtosis84842.957
Mean284200.87
Median Absolute Deviation (MAD)0
Skewness268.35151
Sum6.7584388 × 1010
Variance1.4748148 × 1015
MonotonicityNot monotonic
2023-07-15T18:36:22.711076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237635
99.9%
15000000 3
 
< 0.1%
37500000 2
 
< 0.1%
100000000 2
 
< 0.1%
22250000 2
 
< 0.1%
50000000 2
 
< 0.1%
537766 1
 
< 0.1%
40000000 1
 
< 0.1%
5534101 1
 
< 0.1%
66496099 1
 
< 0.1%
Other values (155) 155
 
0.1%
ValueCountFrequency (%)
0 237635
99.9%
537766 1
 
< 0.1%
2064000 1
 
< 0.1%
2250000 1
 
< 0.1%
3681834 1
 
< 0.1%
4900000 1
 
< 0.1%
5150000 1
 
< 0.1%
5534101 1
 
< 0.1%
5980355 1
 
< 0.1%
7578913 1
 
< 0.1%
ValueCountFrequency (%)
1.380589369 × 10101
< 0.1%
9034652501 1
< 0.1%
4492512530 1
< 0.1%
3941224493 1
< 0.1%
3386758564 1
< 0.1%
2749424729 1
< 0.1%
2075559772 1
< 0.1%
2026289484 1
< 0.1%
1954274210 1
< 0.1%
1269223998 1
< 0.1%

Valor Facturado
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct52155
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25467689
Minimum0
Maximum2.0702827 × 1011
Zeros114088
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:23.012079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2100000
Q314605390
95-th percentile50400000
Maximum2.0702827 × 1011
Range2.0702827 × 1011
Interquartile range (IQR)14605390

Descriptive statistics

Standard deviation5.3329735 × 108
Coefficient of variation (CV)20.940155
Kurtosis96663.912
Mean25467689
Median Absolute Deviation (MAD)2100000
Skewness262.28192
Sum6.0563437 × 1012
Variance2.8440606 × 1017
MonotonicityNot monotonic
2023-07-15T18:36:23.241077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 114088
48.0%
12000000 1231
 
0.5%
6000000 1154
 
0.5%
9000000 1070
 
0.4%
18000000 802
 
0.3%
10000000 781
 
0.3%
15000000 779
 
0.3%
8000000 758
 
0.3%
4000000 636
 
0.3%
7500000 613
 
0.3%
Other values (52145) 115893
48.7%
ValueCountFrequency (%)
0 114088
48.0%
1 1
 
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
11 1
 
< 0.1%
46 1
 
< 0.1%
264 1
 
< 0.1%
675 1
 
< 0.1%
7050 1
 
< 0.1%
22000 1
 
< 0.1%
ValueCountFrequency (%)
2.070282742 × 10111
< 0.1%
6.018851596 × 10101
< 0.1%
4.679517814 × 10101
< 0.1%
3.050190129 × 10101
< 0.1%
2.877481247 × 10101
< 0.1%
2.569428908 × 10101
< 0.1%
2.525695662 × 10101
< 0.1%
2.447199937 × 10101
< 0.1%
2.356861877 × 10101
< 0.1%
2.255936405 × 10101
< 0.1%

Valor Pendiente de Pago
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct76052
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1196645 × 108
Minimum-32912750
Maximum8.612227 × 1011
Zeros44489
Zeros (%)18.7%
Negative72
Negative (%)< 0.1%
Memory size3.6 MiB
2023-07-15T18:36:23.689087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-32912750
5-th percentile0
Q12619000
median12150000
Q327084266
95-th percentile1.11414 × 108
Maximum8.612227 × 1011
Range8.6125561 × 1011
Interquartile range (IQR)24465266

Descriptive statistics

Standard deviation3.1361043 × 109
Coefficient of variation (CV)28.009322
Kurtosis34888.181
Mean1.1196645 × 108
Median Absolute Deviation (MAD)11483334
Skewness160.96999
Sum2.6626182 × 1013
Variance9.8351504 × 1018
MonotonicityNot monotonic
2023-07-15T18:36:23.889084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44489
 
18.7%
12000000 1747
 
0.7%
9000000 1396
 
0.6%
10000000 1243
 
0.5%
18000000 1197
 
0.5%
6000000 1170
 
0.5%
8000000 1129
 
0.5%
15000000 1064
 
0.4%
14000000 1031
 
0.4%
20000000 950
 
0.4%
Other values (76042) 182389
76.7%
ValueCountFrequency (%)
-32912750 1
< 0.1%
-28673333 1
< 0.1%
-24917750 1
< 0.1%
-19683358 1
< 0.1%
-18840334 1
< 0.1%
-16534018 1
< 0.1%
-16308224 1
< 0.1%
-14560000 1
< 0.1%
-13774532 1
< 0.1%
-12480667 1
< 0.1%
ValueCountFrequency (%)
8.61222696 × 10111
< 0.1%
6.300077483 × 10111
< 0.1%
4.547759218 × 10111
< 0.1%
3.448830208 × 10111
< 0.1%
3.19366761 × 10111
< 0.1%
3.190473682 × 10111
< 0.1%
2.449598007 × 10111
< 0.1%
2.231804056 × 10111
< 0.1%
2.202424716 × 10111
< 0.1%
2.11033526 × 10111
< 0.1%

Valor Pagado
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct41558
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18457961
Minimum0
Maximum1.86303 × 1011
Zeros144403
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:24.098089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39846999
95-th percentile41973750
Maximum1.86303 × 1011
Range1.86303 × 1011
Interquartile range (IQR)9846999

Descriptive statistics

Standard deviation4.6956491 × 108
Coefficient of variation (CV)25.439696
Kurtosis105935.94
Mean18457961
Median Absolute Deviation (MAD)0
Skewness281.63166
Sum4.3893954 × 1012
Variance2.2049121 × 1017
MonotonicityNot monotonic
2023-07-15T18:36:24.410089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 144403
60.7%
6000000 790
 
0.3%
12000000 768
 
0.3%
9000000 742
 
0.3%
15000000 589
 
0.2%
10000000 538
 
0.2%
8000000 522
 
0.2%
4000000 504
 
0.2%
18000000 502
 
0.2%
7500000 426
 
0.2%
Other values (41548) 88021
37.0%
ValueCountFrequency (%)
0 144403
60.7%
1 1
 
< 0.1%
3 2
 
< 0.1%
46 1
 
< 0.1%
264 1
 
< 0.1%
7050 1
 
< 0.1%
22000 1
 
< 0.1%
27499 1
 
< 0.1%
35800 1
 
< 0.1%
36325 1
 
< 0.1%
ValueCountFrequency (%)
1.86303005 × 10111
< 0.1%
6.018851596 × 10101
< 0.1%
4.679517814 × 10101
< 0.1%
2.569428908 × 10101
< 0.1%
2.525695662 × 10101
< 0.1%
2.502795119 × 10101
< 0.1%
2.356861877 × 10101
< 0.1%
2.142874612 × 10101
< 0.1%
1.658942382 × 10101
< 0.1%
1.571908889 × 10101
< 0.1%

Valor Amortizado
Real number (ℝ)

SKEWED  ZEROS 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14546.895
Minimum0
Maximum7.1725785 × 108
Zeros237779
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:24.618087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7.1725785 × 108
Range7.1725785 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2369747.2
Coefficient of variation (CV)162.90399
Kurtosis55463.353
Mean14546.895
Median Absolute Deviation (MAD)0
Skewness221.50553
Sum3.4593244 × 109
Variance5.615702 × 1012
MonotonicityNot monotonic
2023-07-15T18:36:24.789091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 237779
> 99.9%
47662948 1
 
< 0.1%
37500000 1
 
< 0.1%
13075620 1
 
< 0.1%
9946822 1
 
< 0.1%
38473586 1
 
< 0.1%
48400000 1
 
< 0.1%
10000000 1
 
< 0.1%
18073700 1
 
< 0.1%
69992544 1
 
< 0.1%
Other values (17) 17
 
< 0.1%
ValueCountFrequency (%)
0 237779
> 99.9%
2392142 1
 
< 0.1%
7140000 1
 
< 0.1%
7993000 1
 
< 0.1%
9946822 1
 
< 0.1%
10000000 1
 
< 0.1%
13075620 1
 
< 0.1%
18073700 1
 
< 0.1%
22250000 1
 
< 0.1%
24200000 1
 
< 0.1%
ValueCountFrequency (%)
717257850 1
< 0.1%
548710541 1
< 0.1%
450789542 1
< 0.1%
292713500 1
< 0.1%
286814675 1
< 0.1%
227650000 1
< 0.1%
197522363 1
< 0.1%
175997600 1
< 0.1%
83500000 1
< 0.1%
69992544 1
< 0.1%

Valor Pendiente de Amortizacion
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct150
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269653.97
Minimum-9946822
Maximum1.3805894 × 1010
Zeros237652
Zeros (%)99.9%
Negative1
Negative (%)< 0.1%
Memory size3.6 MiB
2023-07-15T18:36:25.002087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-9946822
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.3805894 × 1010
Range1.3815841 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38329455
Coefficient of variation (CV)142.14311
Kurtosis85498.454
Mean269653.97
Median Absolute Deviation (MAD)0
Skewness269.85459
Sum6.4125063 × 1010
Variance1.4691471 × 1015
MonotonicityNot monotonic
2023-07-15T18:36:25.219084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237652
99.9%
15000000 3
 
< 0.1%
100000000 2
 
< 0.1%
40000000 2
 
< 0.1%
38473586 1
 
< 0.1%
15960399 1
 
< 0.1%
58400000 1
 
< 0.1%
205039875 1
 
< 0.1%
4900000 1
 
< 0.1%
2064000 1
 
< 0.1%
Other values (140) 140
 
0.1%
ValueCountFrequency (%)
-9946822 1
 
< 0.1%
0 237652
99.9%
537766 1
 
< 0.1%
1260000 1
 
< 0.1%
2064000 1
 
< 0.1%
2250000 1
 
< 0.1%
2537186 1
 
< 0.1%
3588213 1
 
< 0.1%
3681834 1
 
< 0.1%
4900000 1
 
< 0.1%
ValueCountFrequency (%)
1.380589369 × 10101
< 0.1%
9034652501 1
< 0.1%
4492512530 1
< 0.1%
3941224493 1
< 0.1%
3386758564 1
< 0.1%
2749424729 1
< 0.1%
2075559772 1
< 0.1%
2026289484 1
< 0.1%
1954274210 1
< 0.1%
1269223998 1
< 0.1%

Valor Pendiente de Ejecucion
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct76121
Distinct (%)32.1%
Missing856
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1.1228875 × 108
Minimum-2.1847204 × 108
Maximum8.612227 × 1011
Zeros43569
Zeros (%)18.3%
Negative130
Negative (%)0.1%
Memory size3.6 MiB
2023-07-15T18:36:25.446089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.1847204 × 108
5-th percentile0
Q12760000
median12240000
Q327200000
95-th percentile1.1186503 × 108
Maximum8.612227 × 1011
Range8.6144117 × 1011
Interquartile range (IQR)24440000

Descriptive statistics

Standard deviation3.1410417 × 109
Coefficient of variation (CV)27.972898
Kurtosis34794.39
Mean1.1228875 × 108
Median Absolute Deviation (MAD)11422303
Skewness160.78279
Sum2.6606707 × 1013
Variance9.8661431 × 1018
MonotonicityNot monotonic
2023-07-15T18:36:25.664085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43569
 
18.3%
12000000 1746
 
0.7%
9000000 1395
 
0.6%
10000000 1243
 
0.5%
18000000 1200
 
0.5%
6000000 1169
 
0.5%
8000000 1129
 
0.5%
15000000 1065
 
0.4%
14000000 1034
 
0.4%
20000000 948
 
0.4%
Other values (76111) 182451
76.7%
ValueCountFrequency (%)
-218472035 1
< 0.1%
-32912750 1
< 0.1%
-28673333 1
< 0.1%
-24917750 1
< 0.1%
-23720700 1
< 0.1%
-19683358 1
< 0.1%
-18840334 1
< 0.1%
-16534018 1
< 0.1%
-16308224 1
< 0.1%
-14560000 1
< 0.1%
ValueCountFrequency (%)
8.61222696 × 10111
< 0.1%
6.300077483 × 10111
< 0.1%
4.547759218 × 10111
< 0.1%
3.448830208 × 10111
< 0.1%
3.19366761 × 10111
< 0.1%
3.190473682 × 10111
< 0.1%
2.449598007 × 10111
< 0.1%
2.231804056 × 10111
< 0.1%
2.202424716 × 10111
< 0.1%
2.11033526 × 10111
< 0.1%

Saldo CDP
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct72450
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7665435 × 1010
Minimum0
Maximum6.0120229 × 1015
Zeros15160
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:25.887090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111756260
median30149309
Q32.0745022 × 108
95-th percentile5.057437 × 109
Maximum6.0120229 × 1015
Range6.0120229 × 1015
Interquartile range (IQR)1.9569396 × 108

Descriptive statistics

Standard deviation1.2330093 × 1013
Coefficient of variation (CV)445.68587
Kurtosis237681.51
Mean2.7665435 × 1010
Median Absolute Deviation (MAD)24660691
Skewness487.46349
Sum6.5789787 × 1015
Variance1.520312 × 1026
MonotonicityNot monotonic
2023-07-15T18:36:26.104092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15160
 
6.4%
12000000 1596
 
0.7%
15000000 1333
 
0.6%
10000000 1269
 
0.5%
9000000 1198
 
0.5%
18000000 1180
 
0.5%
6000000 1066
 
0.4%
20000000 1055
 
0.4%
8000000 1017
 
0.4%
24000000 984
 
0.4%
Other values (72440) 211947
89.1%
ValueCountFrequency (%)
0 15160
6.4%
1 14
 
< 0.1%
3 1
 
< 0.1%
30 1
 
< 0.1%
155 1
 
< 0.1%
342 1
 
< 0.1%
10000 1
 
< 0.1%
28000 1
 
< 0.1%
58151 1
 
< 0.1%
60000 1
 
< 0.1%
ValueCountFrequency (%)
6.012022895 × 10151
 
< 0.1%
4.301160446 × 10135
< 0.1%
7.9749668 × 10121
 
< 0.1%
4.6401 × 10121
 
< 0.1%
4 × 10121
 
< 0.1%
2.0505 × 10121
 
< 0.1%
1.50022635 × 10121
 
< 0.1%
1.428 × 10126
< 0.1%
1.25329246 × 10121
 
< 0.1%
9.664 × 10111
 
< 0.1%

Saldo Vigencia
Real number (ℝ)

SKEWED  ZEROS 

Distinct2206
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.012015 × 108
Minimum0
Maximum2.1170079 × 1012
Zeros234044
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:26.377088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.1170079 × 1012
Range2.1170079 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7606003 × 1010
Coefficient of variation (CV)34.455756
Kurtosis3431.1455
Mean8.012015 × 108
Median Absolute Deviation (MAD)0
Skewness54.941886
Sum1.9052972 × 1014
Variance7.6209141 × 1020
MonotonicityNot monotonic
2023-07-15T18:36:26.581089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 234044
98.4%
2.841753891 × 1010154
 
0.1%
2.088844511 × 101045
 
< 0.1%
1.251082738 × 101045
 
< 0.1%
4.574000043 × 101037
 
< 0.1%
2.18715 × 101030
 
< 0.1%
4660998260 30
 
< 0.1%
1.471853061 × 101128
 
< 0.1%
1.297436856 × 101122
 
< 0.1%
3.1135462 × 101021
 
< 0.1%
Other values (2196) 3349
 
1.4%
ValueCountFrequency (%)
0 234044
98.4%
35185 1
 
< 0.1%
375000 1
 
< 0.1%
480000 1
 
< 0.1%
666667 1
 
< 0.1%
775727 1
 
< 0.1%
885462 1
 
< 0.1%
950004 1
 
< 0.1%
990000 1
 
< 0.1%
1000000 1
 
< 0.1%
ValueCountFrequency (%)
2.117007936 × 101213
< 0.1%
1.91006 × 10123
 
< 0.1%
1.540093841 × 10121
 
< 0.1%
1.484533775 × 10125
 
< 0.1%
1.476728188 × 10121
 
< 0.1%
1.476449418 × 10124
 
< 0.1%
1.467475861 × 10121
 
< 0.1%
1.46393365 × 10126
< 0.1%
1.462497405 × 10121
 
< 0.1%
1.460496505 × 10121
 
< 0.1%

EsPostConflicto
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
No
235667 
Si
 
1717
ND
 
421

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters475610
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 235667
99.1%
Si 1717
 
0.7%
ND 421
 
0.2%

Length

2023-07-15T18:36:26.780091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:26.945086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 235667
99.1%
si 1717
 
0.7%
nd 421
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 236088
49.6%
o 235667
49.6%
S 1717
 
0.4%
i 1717
 
0.4%
D 421
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 238226
50.1%
Lowercase Letter 237384
49.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 236088
99.1%
S 1717
 
0.7%
D 421
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
o 235667
99.3%
i 1717
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 475610
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 236088
49.6%
o 235667
49.6%
S 1717
 
0.4%
i 1717
 
0.4%
D 421
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 236088
49.6%
o 235667
49.6%
S 1717
 
0.4%
i 1717
 
0.4%
D 421
 
0.1%

Destino Gasto
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Inversión
149010 
Funcionamiento
88316 
No Definido
 
479

Length

Max length14
Median length9
Mean length10.860928
Min length9

Characters and Unicode

Total characters2582783
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInversión
2nd rowInversión
3rd rowInversión
4th rowFuncionamiento
5th rowInversión

Common Values

ValueCountFrequency (%)
Inversión 149010
62.7%
Funcionamiento 88316
37.1%
No Definido 479
 
0.2%

Length

2023-07-15T18:36:27.092103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:27.301087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
inversión 149010
62.5%
funcionamiento 88316
37.1%
no 479
 
0.2%
definido 479
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 563447
21.8%
i 326600
12.6%
e 237805
9.2%
o 177590
 
6.9%
I 149010
 
5.8%
v 149010
 
5.8%
r 149010
 
5.8%
s 149010
 
5.8%
ó 149010
 
5.8%
a 88316
 
3.4%
Other values (10) 443975
17.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2344020
90.8%
Uppercase Letter 238284
 
9.2%
Space Separator 479
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 563447
24.0%
i 326600
13.9%
e 237805
10.1%
o 177590
 
7.6%
v 149010
 
6.4%
r 149010
 
6.4%
s 149010
 
6.4%
ó 149010
 
6.4%
a 88316
 
3.8%
t 88316
 
3.8%
Other values (5) 265906
11.3%
Uppercase Letter
ValueCountFrequency (%)
I 149010
62.5%
F 88316
37.1%
N 479
 
0.2%
D 479
 
0.2%
Space Separator
ValueCountFrequency (%)
479
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2582304
> 99.9%
Common 479
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 563447
21.8%
i 326600
12.6%
e 237805
9.2%
o 177590
 
6.9%
I 149010
 
5.8%
v 149010
 
5.8%
r 149010
 
5.8%
s 149010
 
5.8%
ó 149010
 
5.8%
a 88316
 
3.4%
Other values (9) 443496
17.2%
Common
ValueCountFrequency (%)
479
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2433773
94.2%
None 149010
 
5.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 563447
23.2%
i 326600
13.4%
e 237805
9.8%
o 177590
 
7.3%
I 149010
 
6.1%
v 149010
 
6.1%
r 149010
 
6.1%
s 149010
 
6.1%
a 88316
 
3.6%
t 88316
 
3.6%
Other values (9) 355659
14.6%
None
ValueCountFrequency (%)
ó 149010
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Distribuido
187251 
Recursos Propios
50554 

Length

Max length16
Median length11
Mean length12.06293
Min length11

Characters and Unicode

Total characters2868625
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDistribuido
2nd rowDistribuido
3rd rowRecursos Propios
4th rowDistribuido
5th rowDistribuido

Common Values

ValueCountFrequency (%)
Distribuido 187251
78.7%
Recursos Propios 50554
 
21.3%

Length

2023-07-15T18:36:27.460103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:27.631091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
distribuido 187251
64.9%
recursos 50554
 
17.5%
propios 50554
 
17.5%

Most occurring characters

ValueCountFrequency (%)
i 612307
21.3%
s 338913
11.8%
o 338913
11.8%
r 288359
10.1%
u 237805
 
8.3%
D 187251
 
6.5%
t 187251
 
6.5%
b 187251
 
6.5%
d 187251
 
6.5%
R 50554
 
1.8%
Other values (5) 252770
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2529712
88.2%
Uppercase Letter 288359
 
10.1%
Space Separator 50554
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 612307
24.2%
s 338913
13.4%
o 338913
13.4%
r 288359
11.4%
u 237805
 
9.4%
t 187251
 
7.4%
b 187251
 
7.4%
d 187251
 
7.4%
e 50554
 
2.0%
c 50554
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
D 187251
64.9%
R 50554
 
17.5%
P 50554
 
17.5%
Space Separator
ValueCountFrequency (%)
50554
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2818071
98.2%
Common 50554
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 612307
21.7%
s 338913
12.0%
o 338913
12.0%
r 288359
10.2%
u 237805
 
8.4%
D 187251
 
6.6%
t 187251
 
6.6%
b 187251
 
6.6%
d 187251
 
6.6%
R 50554
 
1.8%
Other values (4) 202216
 
7.2%
Common
ValueCountFrequency (%)
50554
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2868625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 612307
21.3%
s 338913
11.8%
o 338913
11.8%
r 288359
10.1%
u 237805
 
8.3%
D 187251
 
6.5%
t 187251
 
6.5%
b 187251
 
6.5%
d 187251
 
6.5%
R 50554
 
1.8%
Other values (5) 252770
8.8%

Dias Adicionados
Real number (ℝ)

SKEWED  ZEROS 

Distinct280
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1061037
Minimum0
Maximum365334
Zeros203441
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:27.799817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile61
Maximum365334
Range365334
Interquartile range (IQR)0

Descriptive statistics

Standard deviation749.5589
Coefficient of variation (CV)82.313899
Kurtosis237290.48
Mean9.1061037
Median Absolute Deviation (MAD)0
Skewness486.86153
Sum2165477
Variance561838.55
MonotonicityNot monotonic
2023-07-15T18:36:27.993822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 203441
85.5%
31 3827
 
1.6%
61 2612
 
1.1%
30 2566
 
1.1%
92 1879
 
0.8%
91 1276
 
0.5%
15 1099
 
0.5%
60 964
 
0.4%
62 847
 
0.4%
29 597
 
0.3%
Other values (270) 18697
 
7.9%
ValueCountFrequency (%)
0 203441
85.5%
1 446
 
0.2%
2 264
 
0.1%
3 210
 
0.1%
4 222
 
0.1%
5 247
 
0.1%
6 277
 
0.1%
7 296
 
0.1%
8 301
 
0.1%
9 302
 
0.1%
ValueCountFrequency (%)
365334 1
 
< 0.1%
3378 1
 
< 0.1%
457 2
 
< 0.1%
442 1
 
< 0.1%
427 1
 
< 0.1%
423 1
 
< 0.1%
409 1
 
< 0.1%
395 1
 
< 0.1%
368 1
 
< 0.1%
365 35
< 0.1%
Distinct749
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Memory size3.6 MiB
2023-07-15T18:36:28.651375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length46
Median length10
Mean length9.8803028
Min length2

Characters and Unicode

Total characters2349536
Distinct characters69
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique353 ?
Unique (%)0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIANO
3rd rowColombiana
4th rowcolombiano
5th rowCOLOMBIA
ValueCountFrequency (%)
colombiana 132384
54.0%
colombiano 63916
26.1%
colombia 31678
 
12.9%
sin 7111
 
2.9%
descripcion 7111
 
2.9%
colombina 175
 
0.1%
venezolana 113
 
< 0.1%
colobiana 107
 
< 0.1%
venezolano 77
 
< 0.1%
colmbiana 72
 
< 0.1%
Other values (555) 2478
 
1.0%
2023-07-15T18:36:29.621512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 268700
11.4%
o 262866
 
11.2%
A 186989
 
8.0%
C 185495
 
7.9%
a 177691
 
7.6%
i 134122
 
5.7%
L 117473
 
5.0%
I 117381
 
5.0%
B 117252
 
5.0%
M 117069
 
5.0%
Other values (59) 664498
28.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1230449
52.4%
Lowercase Letter 1111128
47.3%
Space Separator 7447
 
0.3%
Decimal Number 512
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 262866
23.7%
a 177691
16.0%
i 134122
12.1%
l 112779
10.1%
b 112547
10.1%
m 112492
10.1%
n 109514
9.9%
c 58806
 
5.3%
e 7588
 
0.7%
r 7361
 
0.7%
Other values (21) 15362
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
O 268700
21.8%
A 186989
15.2%
C 185495
15.1%
L 117473
9.5%
I 117381
9.5%
B 117252
9.5%
M 117069
9.5%
N 103621
 
8.4%
S 7317
 
0.6%
D 7227
 
0.6%
Other values (17) 1925
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 88
17.2%
1 82
16.0%
3 55
10.7%
5 50
9.8%
6 46
9.0%
2 41
8.0%
8 39
7.6%
7 39
7.6%
9 38
7.4%
4 34
 
6.6%
Space Separator
ValueCountFrequency (%)
7447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2341577
99.7%
Common 7959
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 268700
11.5%
o 262866
11.2%
A 186989
 
8.0%
C 185495
 
7.9%
a 177691
 
7.6%
i 134122
 
5.7%
L 117473
 
5.0%
I 117381
 
5.0%
B 117252
 
5.0%
M 117069
 
5.0%
Other values (48) 656539
28.0%
Common
ValueCountFrequency (%)
7447
93.6%
0 88
 
1.1%
1 82
 
1.0%
3 55
 
0.7%
5 50
 
0.6%
6 46
 
0.6%
2 41
 
0.5%
8 39
 
0.5%
7 39
 
0.5%
9 38
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2349390
> 99.9%
None 146
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 268700
11.4%
o 262866
 
11.2%
A 186989
 
8.0%
C 185495
 
7.9%
a 177691
 
7.6%
i 134122
 
5.7%
L 117473
 
5.0%
I 117381
 
5.0%
B 117252
 
5.0%
M 117069
 
5.0%
Other values (51) 664352
28.3%
None
ValueCountFrequency (%)
Ñ 89
61.0%
ñ 31
 
21.2%
á 15
 
10.3%
ô 3
 
2.1%
é 3
 
2.1%
í 3
 
2.1%
Á 1
 
0.7%
ú 1
 
0.7%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
No Definido
112298 
Mujer
66134 
Hombre
59026 
Otro
 
347

Length

Max length11
Median length6
Mean length8.0801161
Min length4

Characters and Unicode

Total characters1921492
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHombre
2nd rowHombre
3rd rowMujer
4th rowHombre
5th rowHombre

Common Values

ValueCountFrequency (%)
No Definido 112298
47.2%
Mujer 66134
27.8%
Hombre 59026
24.8%
Otro 347
 
0.1%

Length

2023-07-15T18:36:29.855078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-15T18:36:30.070694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 112298
32.1%
definido 112298
32.1%
mujer 66134
18.9%
hombre 59026
16.9%
otro 347
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 283969
14.8%
e 237458
12.4%
i 224596
11.7%
r 125507
 
6.5%
N 112298
 
5.8%
112298
 
5.8%
D 112298
 
5.8%
f 112298
 
5.8%
n 112298
 
5.8%
d 112298
 
5.8%
Other values (8) 376174
19.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1459091
75.9%
Uppercase Letter 350103
 
18.2%
Space Separator 112298
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 283969
19.5%
e 237458
16.3%
i 224596
15.4%
r 125507
8.6%
f 112298
 
7.7%
n 112298
 
7.7%
d 112298
 
7.7%
j 66134
 
4.5%
u 66134
 
4.5%
m 59026
 
4.0%
Other values (2) 59373
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
N 112298
32.1%
D 112298
32.1%
M 66134
18.9%
H 59026
16.9%
O 347
 
0.1%
Space Separator
ValueCountFrequency (%)
112298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1809194
94.2%
Common 112298
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 283969
15.7%
e 237458
13.1%
i 224596
12.4%
r 125507
6.9%
N 112298
 
6.2%
D 112298
 
6.2%
f 112298
 
6.2%
n 112298
 
6.2%
d 112298
 
6.2%
j 66134
 
3.7%
Other values (7) 310040
17.1%
Common
ValueCountFrequency (%)
112298
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1921492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 283969
14.8%
e 237458
12.4%
i 224596
11.7%
r 125507
 
6.5%
N 112298
 
5.8%
112298
 
5.8%
D 112298
 
5.8%
f 112298
 
5.8%
n 112298
 
5.8%
d 112298
 
5.8%
Other values (8) 376174
19.6%

Presupuesto General de la Nacion – PGN
Real number (ℝ)

SKEWED  ZEROS 

Distinct26991
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49672739
Minimum0
Maximum8.612227 × 1011
Zeros186497
Zeros (%)78.4%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:30.332213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile57376282
Maximum8.612227 × 1011
Range8.612227 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7020287 × 109
Coefficient of variation (CV)54.396613
Kurtosis60681.287
Mean49672739
Median Absolute Deviation (MAD)0
Skewness225.67553
Sum1.1812426 × 1013
Variance7.3009594 × 1018
MonotonicityNot monotonic
2023-07-15T18:36:30.576240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 186497
78.4%
30210840 249
 
0.1%
20000000 215
 
0.1%
30000000 210
 
0.1%
24000000 196
 
0.1%
18000000 164
 
0.1%
12000000 159
 
0.1%
15000000 153
 
0.1%
28000000 146
 
0.1%
40000000 134
 
0.1%
Other values (26981) 49682
 
20.9%
ValueCountFrequency (%)
0 186497
78.4%
1 2
 
< 0.1%
22262 1
 
< 0.1%
31000 1
 
< 0.1%
75291 1
 
< 0.1%
93016 1
 
< 0.1%
100000 1
 
< 0.1%
142450 1
 
< 0.1%
192200 1
 
< 0.1%
205000 1
 
< 0.1%
ValueCountFrequency (%)
8.61222696 × 10111
< 0.1%
6.300077483 × 10111
< 0.1%
4.547759218 × 10111
< 0.1%
3.19366761 × 10111
< 0.1%
2.231804056 × 10111
< 0.1%
2.126830241 × 10111
< 0.1%
2.11033526 × 10111
< 0.1%
2.05923159 × 10111
< 0.1%
9.006472657 × 10101
< 0.1%
8 × 10101
< 0.1%

Sistema General de Participaciones
Real number (ℝ)

SKEWED  ZEROS 

Distinct4653
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4812445.2
Minimum0
Maximum6.3356333 × 1010
Zeros226862
Zeros (%)95.4%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:30.832767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.3356333 × 1010
Range6.3356333 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1274757 × 108
Coefficient of variation (CV)44.207791
Kurtosis39939.011
Mean4812445.2
Median Absolute Deviation (MAD)0
Skewness167.48456
Sum1.1444235 × 1012
Variance4.5261529 × 1016
MonotonicityNot monotonic
2023-07-15T18:36:31.060765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 226862
95.4%
12000000 138
 
0.1%
18000000 138
 
0.1%
6000000 110
 
< 0.1%
10000000 110
 
< 0.1%
21600000 98
 
< 0.1%
9000000 97
 
< 0.1%
10800000 96
 
< 0.1%
14000000 86
 
< 0.1%
24000000 83
 
< 0.1%
Other values (4643) 9987
 
4.2%
ValueCountFrequency (%)
0 226862
95.4%
30100 1
 
< 0.1%
54619 1
 
< 0.1%
99120 1
 
< 0.1%
142800 1
 
< 0.1%
235620 1
 
< 0.1%
242150 1
 
< 0.1%
243950 1
 
< 0.1%
300000 1
 
< 0.1%
302516 1
 
< 0.1%
ValueCountFrequency (%)
6.335633259 × 10101
< 0.1%
3.251915806 × 10101
< 0.1%
3.249637415 × 10101
< 0.1%
2.75 × 10101
< 0.1%
2.16158336 × 10101
< 0.1%
1.570363687 × 10101
< 0.1%
1.495230124 × 10101
< 0.1%
1.338566467 × 10101
< 0.1%
1.330661797 × 10101
< 0.1%
1.265365792 × 10101
< 0.1%

Sistema General de Regalías
Real number (ℝ)

SKEWED  ZEROS 

Distinct1580
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4292306.6
Minimum0
Maximum6.2707126 × 1010
Zeros235635
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:31.320781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.2707126 × 1010
Range6.2707126 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7991602 × 108
Coefficient of variation (CV)65.213425
Kurtosis23939.198
Mean4292306.6
Median Absolute Deviation (MAD)0
Skewness138.5649
Sum1.020732 × 1012
Variance7.8352978 × 1016
MonotonicityNot monotonic
2023-07-15T18:36:31.770768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 235635
99.1%
50000000 26
 
< 0.1%
41815126 19
 
< 0.1%
24000000 16
 
< 0.1%
15000000 14
 
< 0.1%
9000000 14
 
< 0.1%
90000000 14
 
< 0.1%
20000000 13
 
< 0.1%
16000000 12
 
< 0.1%
36000000 11
 
< 0.1%
Other values (1570) 2031
 
0.9%
ValueCountFrequency (%)
0 235635
99.1%
201348 1
 
< 0.1%
243000 1
 
< 0.1%
277508 1
 
< 0.1%
291325 1
 
< 0.1%
464100 1
 
< 0.1%
511700 1
 
< 0.1%
600000 1
 
< 0.1%
672298 1
 
< 0.1%
966100 1
 
< 0.1%
ValueCountFrequency (%)
6.270712637 × 10101
< 0.1%
5.438763671 × 10101
< 0.1%
4.49056133 × 10101
< 0.1%
4.057312922 × 10101
< 0.1%
3.630953781 × 10101
< 0.1%
3.004220165 × 10101
< 0.1%
2.315727523 × 10101
< 0.1%
2.258663125 × 10101
< 0.1%
2.078511343 × 10101
< 0.1%
1.865689912 × 10101
< 0.1%
Distinct34433
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3897364 × 108
Minimum0
Maximum1.4207686 × 1014
Zeros116337
Zeros (%)48.9%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:31.980766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2400000
Q316371000
95-th percentile56000000
Maximum1.4207686 × 1014
Range1.4207686 × 1014
Interquartile range (IQR)16371000

Descriptive statistics

Standard deviation2.9135132 × 1011
Coefficient of variation (CV)455.96767
Kurtosis237795.69
Mean6.3897364 × 108
Median Absolute Deviation (MAD)2400000
Skewness487.63824
Sum1.5195113 × 1014
Variance8.4885593 × 1022
MonotonicityNot monotonic
2023-07-15T18:36:32.210764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116337
48.9%
12000000 1714
 
0.7%
9000000 1300
 
0.5%
18000000 1281
 
0.5%
6000000 1087
 
0.5%
10000000 1058
 
0.4%
15000000 1013
 
0.4%
14000000 987
 
0.4%
8000000 986
 
0.4%
21000000 935
 
0.4%
Other values (34423) 111107
46.7%
ValueCountFrequency (%)
0 116337
48.9%
1 14
 
< 0.1%
3000 1
 
< 0.1%
3703 1
 
< 0.1%
7676 1
 
< 0.1%
10000 1
 
< 0.1%
16824 1
 
< 0.1%
18674 1
 
< 0.1%
46470 1
 
< 0.1%
85400 1
 
< 0.1%
ValueCountFrequency (%)
1.420768642 × 10141
< 0.1%
3.448830208 × 10111
< 0.1%
3.190473682 × 10111
< 0.1%
2.202424716 × 10111
< 0.1%
2.021478 × 10111
< 0.1%
1.239773493 × 10111
< 0.1%
7.059183664 × 10101
< 0.1%
6.884904898 × 10101
< 0.1%
5.858519574 × 10101
< 0.1%
5.461379556 × 10101
< 0.1%

Recursos de Credito
Real number (ℝ)

SKEWED  ZEROS 

Distinct600
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4640038.2
Minimum0
Maximum1 × 1011
Zeros237029
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:32.460767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1 × 1011
Range1 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9824754 × 108
Coefficient of variation (CV)85.828506
Kurtosis31629.839
Mean4640038.2
Median Absolute Deviation (MAD)0
Skewness159.55737
Sum1.1034243 × 1012
Variance1.5860111 × 1017
MonotonicityNot monotonic
2023-07-15T18:36:32.685765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237029
99.7%
3642000 24
 
< 0.1%
40000000 10
 
< 0.1%
9870000 10
 
< 0.1%
19650000 9
 
< 0.1%
27510000 7
 
< 0.1%
19554000 7
 
< 0.1%
6739833 6
 
< 0.1%
4368000 5
 
< 0.1%
5000000 5
 
< 0.1%
Other values (590) 693
 
0.3%
ValueCountFrequency (%)
0 237029
99.7%
325000 1
 
< 0.1%
924420 1
 
< 0.1%
1000000 1
 
< 0.1%
1057809 1
 
< 0.1%
1440000 1
 
< 0.1%
1500000 1
 
< 0.1%
1586714 1
 
< 0.1%
1764730 1
 
< 0.1%
1800000 1
 
< 0.1%
ValueCountFrequency (%)
1 × 10111
< 0.1%
8.7 × 10101
< 0.1%
5.662034 × 10101
< 0.1%
4.9 × 10101
< 0.1%
4.5 × 10101
< 0.1%
4.335985846 × 10101
< 0.1%
4.2 × 10101
< 0.1%
3.414566186 × 10101
< 0.1%
3.386 × 10101
< 0.1%
3.328558224 × 10101
< 0.1%

Recursos Propios
Real number (ℝ)

SKEWED  ZEROS 

Distinct24742
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25331267
Minimum0
Maximum1.9880246 × 1011
Zeros185428
Zeros (%)78.0%
Negative0
Negative (%)0.0%
Memory size3.6 MiB
2023-07-15T18:36:32.919766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile43400000
Maximum1.9880246 × 1011
Range1.9880246 × 1011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.3922992 × 108
Coefficient of variation (CV)33.130199
Kurtosis26710.205
Mean25331267
Median Absolute Deviation (MAD)0
Skewness137.3735
Sum6.023902 × 1012
Variance7.0430685 × 1017
MonotonicityNot monotonic
2023-07-15T18:36:33.164765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 185428
78.0%
12000000 394
 
0.2%
9000000 355
 
0.1%
10000000 355
 
0.1%
15000000 340
 
0.1%
6000000 317
 
0.1%
18000000 288
 
0.1%
24000000 284
 
0.1%
20000000 276
 
0.1%
30000000 268
 
0.1%
Other values (24732) 49500
 
20.8%
ValueCountFrequency (%)
0 185428
78.0%
1 7
 
< 0.1%
11090 1
 
< 0.1%
16000 1
 
< 0.1%
26386 1
 
< 0.1%
29000 1
 
< 0.1%
54000 1
 
< 0.1%
70000 1
 
< 0.1%
94087 4
 
< 0.1%
100000 1
 
< 0.1%
ValueCountFrequency (%)
1.988024598 × 10111
< 0.1%
1.896624457 × 10111
< 0.1%
9.468352547 × 10101
< 0.1%
8.042395266 × 10101
< 0.1%
7.48008191 × 10101
< 0.1%
7.233468206 × 10101
< 0.1%
6.934623875 × 10101
< 0.1%
6.240813326 × 10101
< 0.1%
5.785096986 × 10101
< 0.1%
5.391560575 × 10101
< 0.1%

Fecha Inicio Liquidacion
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing237805
Missing (%)100.0%
Memory size3.6 MiB

Fecha Fin Liquidacion
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing237805
Missing (%)100.0%
Memory size3.6 MiB

Interactions

2023-07-15T18:35:59.190514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:47.018764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:51.798404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:56.336402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:00.844294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:05.132294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:09.057298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:13.481477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:17.807477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:22.010480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:26.056477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:30.226512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:34.178510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:38.394532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:42.405511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:46.634535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:50.591530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:54.858510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:59.386514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:47.315423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:52.013412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:56.984052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:01.049296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:05.316294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:09.271296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:13.700496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:18.008480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:22.245478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:26.316479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:30.446513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:34.380516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:38.589513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:42.606514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:46.837513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:50.834511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:55.057511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:59.610510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:47.527426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:52.273401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:57.242642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:01.315304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:05.558295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:09.486295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:13.929480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:18.261475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:22.468476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:26.537480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:30.719508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:34.631510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:38.864511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:42.835512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:47.058514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:51.082511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:55.276510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:59.838527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:48.434409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:52.506400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:57.453640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:01.528294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:05.809306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:09.703294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:14.163476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:18.487478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:22.697476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:26.760497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:30.949510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:34.899513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:39.076519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:43.050526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:47.297514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:51.360511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:55.509512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:00.062508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:48.647402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:52.723401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:57.649639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:01.707295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:06.045297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:09.900332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:14.372476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:18.697483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:22.911477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:26.963492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:31.191532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:35.114511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:39.288514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:43.254532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:47.489543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:51.574527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:55.718513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:00.303511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:48.842401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:52.939403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:57.856643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:01.900301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:06.300295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:10.099347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:14.590475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:18.903499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:23.108478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:27.180477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:31.389534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:35.344514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:39.469536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:43.449511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:47.694510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:51.787511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:55.971511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:00.532511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:49.051403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:53.182402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:58.086641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:02.460296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:06.517295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:10.339396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:14.813477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:19.124477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:23.336481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:27.389475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:31.594515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:35.595513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:39.682528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:43.675515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:47.945515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:52.028511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:56.304508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:00.795517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:49.267418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:53.390402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:58.322642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-15T18:35:06.723301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:10.595401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:15.023493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:19.349492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:23.564481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:27.597476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-15T18:34:49.672403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:53.831421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:58.775637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:03.208294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:07.160292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:11.112861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:15.470477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:19.777481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:24.011476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:28.025479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:32.256512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:36.359518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:40.328512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:44.593515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:48.620526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:52.750510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:56.944529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:01.601284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:49.888400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:54.057402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:58.991638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:03.426295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:07.354316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:11.381379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:15.969478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:19.990474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:24.232475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:28.253479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:32.456511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:36.575511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:40.557513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:44.812512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:48.830512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:52.968510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:57.181510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:01.807304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:50.085405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:54.331421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:59.216641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:03.611314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:07.557295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:11.639375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:16.232479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:20.232476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:24.429480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:28.451480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:32.661511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:36.789512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:40.814518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:45.011516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:49.030512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:53.199517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:57.386527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:02.026013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:50.310399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:54.564401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:59.449292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:03.819311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:07.770294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:11.868395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:16.449478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:20.455484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:24.687479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:28.670480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:32.871509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:37.012510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:41.052515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:45.252512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:49.258508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:53.437512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:57.833509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:02.269223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:50.561401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:54.768400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:59.652295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:04.017302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:07.957299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:12.094377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:16.659478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:20.706479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:24.890497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:28.857493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:33.077511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:37.239510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:41.310513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:45.461513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:49.459510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:53.657511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:58.030508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:02.501229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:50.822421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:54.983416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:59.872294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:04.228294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:08.205295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:12.465375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:16.874479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:20.977477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:25.117477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:29.078492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:33.293527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:37.455512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:41.512531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:45.697512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:49.665519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:53.887527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:58.272513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:02.756222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:51.080404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:55.293411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:00.087295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:04.432297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:08.400294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:12.712465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:17.112476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:21.320477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:25.330498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:29.310497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:33.494527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:37.678515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:41.717527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:45.916511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:49.869514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:54.128518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:58.530509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:02.993221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:51.374400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:55.729401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:00.342298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:04.697297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:08.634295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:12.988465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:17.362477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:21.560476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:25.563484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:29.777535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:33.721532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:37.917515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:41.944535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:46.166513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:50.091513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:54.380511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:58.754512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:36:03.230223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:51.574402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:34:56.049406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:00.584296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:04.907294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:08.831316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:13.241479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:17.565477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:21.778497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:25.801476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:29.981514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:33.927511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:38.144514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:42.159514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:46.402514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:50.309512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:54.610513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-15T18:35:58.967513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-15T18:36:33.424764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nit EntidadValor del ContratoValor de pago adelantadoValor FacturadoValor Pendiente de PagoValor PagadoValor AmortizadoValor Pendiente de AmortizacionValor Pendiente de EjecucionSaldo CDPSaldo VigenciaDias AdicionadosPresupuesto General de la Nacion – PGNSistema General de ParticipacionesSistema General de RegalíasRecursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)Recursos de CreditoRecursos PropiosDepartamentoOrdenSectorRamaEntidad CentralizadaEstado ContratoTipo de ContratoModalidad de ContratacionCondiciones de EntregaTipoDocProveedorEs GrupoEs PymeHabilita Pago AdelantadoLiquidaciónObligación AmbientalObligaciones PostconsumoReversionEsPostConflictoDestino GastoOrigen de los RecursosGénero Representante Legal
Nit Entidad1.0000.190-0.0050.0270.1200.044-0.006-0.0040.1210.1630.0310.1040.155-0.0300.017-0.0890.0070.0570.2620.0590.2630.0160.0840.0590.0300.0550.0390.0180.0110.0160.0000.0410.0470.0040.0000.0190.0110.0230.028
Valor del Contrato0.1901.0000.0360.2620.6330.1790.0130.0330.6390.5120.1030.1260.319-0.0220.0470.0900.0300.0790.0000.0000.0000.0030.0000.0020.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.000
Valor de pago adelantado-0.0050.0361.0000.0020.029-0.0040.3760.9420.0290.0170.000-0.001-0.0120.0130.0210.005-0.0020.0150.0150.0000.0000.0000.0000.0180.0250.0550.0000.0190.0570.0010.1160.0120.0160.0000.0000.0000.0000.0000.000
Valor Facturado0.0270.2620.0021.000-0.3290.8070.017-0.003-0.3350.137-0.0330.0880.1830.021-0.011-0.0170.009-0.0530.0000.0000.0000.0000.0110.0040.0160.0420.0000.0080.0640.0000.0000.0160.0000.0000.0000.0000.0000.0000.000
Valor Pendiente de Pago0.1200.6330.029-0.3291.000-0.510-0.0050.0321.0000.3370.1130.0190.144-0.0160.0500.0810.0260.0900.0000.0000.0050.0000.0060.0000.0620.0380.0000.0150.0640.0000.0000.0100.0000.0000.0000.0000.0000.0000.000
Valor Pagado0.0440.179-0.0040.807-0.5101.0000.019-0.010-0.5160.073-0.0290.0760.163-0.001-0.017-0.034-0.002-0.0400.0000.0000.0000.0000.0110.0000.0110.0350.0000.0050.0550.0030.0000.0140.0000.0000.0000.0000.0000.0000.000
Valor Amortizado-0.0060.0130.3760.017-0.0050.0191.0000.095-0.0050.006-0.0010.000-0.0030.0040.0030.002-0.0010.0070.0090.0180.0000.0040.0000.0000.0170.0150.0000.0000.0330.0060.1690.0050.0000.0000.0000.0000.0000.0070.000
Valor Pendiente de Amortizacion-0.0040.0330.942-0.0030.032-0.0100.0951.0000.0320.0160.001-0.000-0.0110.0120.0200.005-0.0010.0140.0150.0000.0000.0000.0000.0180.0250.0550.0000.0190.0570.0010.1160.0120.0160.0000.0000.0000.0000.0000.000
Valor Pendiente de Ejecucion0.1210.6390.029-0.3351.000-0.516-0.0050.0321.0000.3400.1130.0190.145-0.0160.0500.0820.0270.0900.0000.0000.0050.0000.0060.0000.0620.0380.0000.0150.0640.0000.0000.0100.0000.0000.0000.0000.0000.0000.000
Saldo CDP0.1630.5120.0170.1370.3370.0730.0060.0160.3401.0000.0220.0910.243-0.0290.0240.0240.0190.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Saldo Vigencia0.0310.1030.000-0.0330.113-0.029-0.0010.0010.1130.0221.0000.0200.120-0.017-0.003-0.0570.0080.0100.0330.0280.0310.0530.0080.0060.0420.0490.0020.0210.0260.0110.0000.0590.0060.0000.0000.0000.0130.0090.011
Dias Adicionados0.1040.126-0.0010.0880.0190.0760.000-0.0000.0190.0910.0201.0000.0270.0040.002-0.0080.0030.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.000
Presupuesto General de la Nacion – PGN0.1550.319-0.0120.1830.1440.163-0.003-0.0110.1450.2430.1200.0271.000-0.111-0.038-0.485-0.023-0.2490.0000.0040.0010.0000.0020.0000.0190.0400.0000.0070.0770.0000.0000.0060.0000.0000.0000.0000.0000.0000.000
Sistema General de Participaciones-0.030-0.0220.0130.021-0.016-0.0010.0040.012-0.016-0.029-0.0170.004-0.1111.000-0.019-0.180-0.009-0.1080.0130.0000.0000.0000.0130.0130.0000.0320.0000.0090.0240.0000.0000.0090.0000.0000.0000.0000.0000.0010.000
Sistema General de Regalías0.0170.0470.021-0.0110.050-0.0170.0030.0200.0500.024-0.0030.002-0.038-0.0191.000-0.088-0.003-0.0470.0110.0000.0000.0000.0000.0220.0260.0680.0000.0220.0960.0110.0140.0150.0000.0000.0000.0000.0000.0000.000
Recursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)-0.0890.0900.005-0.0170.081-0.0340.0020.0050.0820.024-0.057-0.008-0.485-0.180-0.0881.000-0.045-0.4720.0000.0000.0000.0030.0000.0020.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.000
Recursos de Credito0.0070.030-0.0020.0090.026-0.002-0.001-0.0010.0270.0190.0080.003-0.023-0.009-0.003-0.0451.000-0.0260.0000.0000.0140.0000.0000.0030.1580.0310.1090.0160.0590.0000.0000.0100.0050.0600.0000.0000.0000.0000.003
Recursos Propios0.0570.0790.015-0.0530.090-0.0400.0070.0140.0900.0020.0100.053-0.249-0.108-0.047-0.472-0.0261.0000.0000.0050.0100.0000.0060.0000.0320.0520.0000.0270.0730.0000.0000.0190.0000.0000.0000.0000.0020.0240.006
Departamento0.2620.0000.0150.0000.0000.0000.0090.0150.0000.0000.0330.0000.0000.0130.0110.0000.0000.0001.0000.2790.1730.1200.2900.1350.0350.0580.1270.0470.0380.0810.0490.2260.1380.0280.0140.0800.1410.2130.142
Orden0.0590.0000.0000.0000.0000.0000.0180.0000.0000.0000.0280.0000.0040.0000.0000.0000.0000.0050.2791.0000.5820.2420.1430.0700.0920.0990.1020.0390.0120.0510.0120.0210.1480.0270.0060.0860.0390.2300.114
Sector0.2630.0000.0000.0000.0050.0000.0000.0000.0050.0000.0310.0000.0010.0000.0000.0000.0140.0100.1730.5821.0000.3610.3500.0910.0840.1830.1250.0590.0370.1250.0200.2120.3480.0700.0110.3550.3290.4510.126
Rama0.0160.0030.0000.0000.0000.0000.0040.0000.0000.0000.0530.0000.0000.0000.0000.0030.0000.0000.1200.2420.3611.0000.0540.0180.1540.1460.0660.0510.0130.0620.0060.1050.0690.0140.0000.0110.1820.3280.049
Entidad Centralizada0.0840.0000.0000.0110.0060.0110.0000.0000.0060.0000.0080.0000.0020.0130.0000.0000.0000.0060.2900.1430.3500.0541.0000.0360.0860.1500.0990.0200.0140.0350.0060.0090.0760.0120.0000.0150.0620.1850.051
Estado Contrato0.0590.0020.0180.0040.0000.0000.0000.0180.0000.0000.0060.0000.0000.0130.0220.0020.0030.0000.1350.0700.0910.0180.0361.0000.0670.0890.0350.0350.1090.0520.0170.0470.0280.0050.0080.0280.0590.0490.051
Tipo de Contrato0.0300.0000.0250.0160.0620.0110.0170.0250.0620.0000.0420.0000.0190.0000.0260.0000.1580.0320.0350.0920.0840.1540.0860.0671.0000.5730.0300.2460.4180.3680.3130.2830.0380.0230.0100.0110.2790.1530.110
Modalidad de Contratacion0.0550.0000.0550.0420.0380.0350.0150.0550.0380.0000.0490.0000.0400.0320.0680.0000.0310.0520.0580.0990.1830.1460.1500.0890.5731.0000.0420.2530.5510.4680.1610.3170.0610.0340.0030.0120.3530.3360.114
Condiciones de Entrega0.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.1090.0000.1270.1020.1250.0660.0990.0350.0300.0421.0000.0240.0290.0610.0010.1220.3120.0540.0050.0400.0420.1010.041
TipoDocProveedor0.0180.0000.0190.0080.0150.0050.0000.0190.0150.0000.0210.0000.0070.0090.0220.0000.0160.0270.0470.0390.0590.0510.0200.0350.2460.2530.0241.0000.3950.3900.0360.3250.0040.0140.0030.0090.0610.0100.144
Es Grupo0.0110.0000.0570.0640.0640.0550.0330.0570.0640.0000.0260.0000.0770.0240.0960.0000.0590.0730.0380.0120.0370.0130.0140.1090.4180.5510.0290.3951.0000.0230.0370.1000.0080.0000.0000.0000.0200.0000.063
Es Pyme0.0160.0010.0010.0000.0000.0030.0060.0010.0000.0000.0110.0000.0000.0000.0110.0010.0000.0000.0810.0510.1250.0620.0350.0520.3680.4680.0610.3900.0231.0000.0210.1530.0390.0110.0030.0150.0500.0000.160
Habilita Pago Adelantado0.0000.0000.1160.0000.0000.0000.1690.1160.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0490.0120.0200.0060.0060.0170.3130.1610.0010.0360.0370.0211.0000.0160.0070.0080.0000.0020.0080.0100.000
Liquidación0.0410.0000.0120.0160.0100.0140.0050.0120.0100.0000.0590.0000.0060.0090.0150.0000.0100.0190.2260.0210.2120.1050.0090.0470.2830.3170.1220.3250.1000.1530.0161.0000.0390.0140.0150.0120.0660.0360.077
Obligación Ambiental0.0470.0000.0160.0000.0000.0000.0000.0160.0000.0000.0060.0060.0000.0000.0000.0000.0050.0000.1380.1480.3480.0690.0760.0280.0380.0610.3120.0040.0080.0390.0070.0391.0000.0870.0070.0120.0740.0200.058
Obligaciones Postconsumo0.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0280.0270.0700.0140.0120.0050.0230.0340.0540.0140.0000.0110.0080.0140.0871.0000.0400.0000.0180.0090.007
Reversion0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0060.0110.0000.0000.0080.0100.0030.0050.0030.0000.0030.0000.0150.0070.0401.0000.0000.0000.0000.000
EsPostConflicto0.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0860.3550.0110.0150.0280.0110.0120.0400.0090.0000.0150.0020.0120.0120.0000.0001.0000.6630.0490.010
Destino Gasto0.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0020.1410.0390.3290.1820.0620.0590.2790.3530.0420.0610.0200.0500.0080.0660.0740.0180.0000.6631.0000.1870.032
Origen de los Recursos0.0230.0000.0000.0000.0000.0000.0070.0000.0000.0000.0090.0000.0000.0010.0000.0000.0000.0240.2130.2300.4510.3280.1850.0490.1530.3360.1010.0100.0000.0000.0100.0360.0200.0090.0000.0490.1871.0000.049
Género Representante Legal0.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0030.0060.1420.1140.1260.0490.0510.0510.1100.1140.0410.1440.0630.1600.0000.0770.0580.0070.0000.0100.0320.0491.000

Missing values

2023-07-15T18:36:03.869234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-15T18:36:05.817224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-15T18:36:08.274596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Nombre EntidadNit EntidadDepartamentoCiudadLocalizaciónOrdenSectorRamaEntidad CentralizadaEstado ContratoTipo de ContratoModalidad de ContratacionFecha de FirmaFecha de Inicio del ContratoFecha de Fin del ContratoFecha de Inicio de EjecucionFecha de Fin de EjecucionCondiciones de EntregaTipoDocProveedorEs GrupoEs PymeHabilita Pago AdelantadoLiquidaciónObligación AmbientalObligaciones PostconsumoReversionValor del ContratoValor de pago adelantadoValor FacturadoValor Pendiente de PagoValor PagadoValor AmortizadoValor Pendiente de AmortizacionValor Pendiente de EjecucionSaldo CDPSaldo VigenciaEsPostConflictoDestino GastoOrigen de los RecursosDias AdicionadosNacionalidad Representante LegalGénero Representante LegalPresupuesto General de la Nacion – PGNSistema General de ParticipacionesSistema General de RegalíasRecursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)Recursos de CreditoRecursos PropiosFecha Inicio LiquidacionFecha Fin Liquidacion
402347UNIDAD EJECUTORA DE SANEAMIENTO DEL VALLE DEL CAUCA805018833Valle del CaucaCaliColombia, Valle del Cauca , CaliTerritorialSalud y Protección SocialEjecutivoCentralizadaEn ejecuciónPrestación de serviciosContratación directa2023-02-192023-02-202023-07-31NaTNaTA convenirCédula de CiudadaníaNoNoNoNoNoNoNo22440000018700000748000014960000007480000.0224400000NoInversiónDistribuido0COLOMBIAHombre0002244000000NaTNaT
1743815Secretaría de Gobierno Convivencia y Seguridad Alcaldía de Tuluá891900272Valle del CaucaTuluáColombia, Valle del Cauca , TuluáTerritorialServicio PúblicoEjecutivoDescentralizadaEn ejecuciónPrestación de serviciosContratación directa2022-09-292022-10-082022-12-15NaTNaTA convenirCédula de CiudadaníaNoNoNoNoNoNoNo64500000064500000006450000.02064000000NoInversiónDistribuido0COLOMBIANOHombre000645000000NaTNaT
88299SECRETARIA DISTRITAL DE AMBIENTE899999061Distrito Capital de BogotáBogotáColombia, Bogotá, BogotáTerritorialAmbiente y Desarrollo SostenibleEjecutivoCentralizadaCerradoPrestación de serviciosContratación directa2022-01-162022-01-212023-02-28NaTNaTA convenirCédula de CiudadaníaNoNoNoNoNoNoNo40133333040133333040133333000.0401333330NoInversiónRecursos Propios0ColombianaMujer0000040133333NaTNaT
2070712GOBERNACIÓN DEL DEPARTAMENTO ARCHIPIELAGO DE SAN ANDRES PROVIDENCIA Y SANTA CATALINA892400038San Andrés, Providencia y Santa CatalinaSan AndrésColombia, San Andrés, Providencia y Santa Catalina , San AndrésTerritorialNo aplica/No perteneceEjecutivoDescentralizadaEn ejecuciónPrestación de serviciosContratación directa2022-09-302022-09-302022-12-29NaTNaTA convenirCédula de CiudadaníaNoNoNoNoNoNoNo12028695001202869500012028695.0163982600NoFuncionamientoDistribuido0colombianoHombre0001202869500NaTNaT
996719ALCALDIA MUNICIPAL DE MELGAR890701933TolimaMelgarColombia, Tolima , MelgarTerritorialNo aplica/No perteneceEjecutivoCentralizadaEn ejecuciónPrestación de serviciosContratación directa2023-01-202023-02-012023-05-31NaTNaTComo acordado previamenteCédula de CiudadaníaNoNoNoNoNoNoNo56000000560000005600000000.056000000NoInversiónDistribuido0COLOMBIAHombre000560000000NaTNaT
441346HMI802013023AtlánticoSoledadColombia, Atlántico , SoledadTerritorialSalud y Protección SocialCorporación AutónomaDescentralizadaEn ejecuciónDecreeLaw092/2017Contratación régimen especial2022-12-192022-12-192022-12-31NaTNaTComo acordado previamenteCédula de CiudadaníaNoNoNoNoNoNoNo30720000030720000003072000.030720000NoFuncionamientoRecursos Propios0COLOMBIANOHombre000003072000NaTNaT
2359637ALCALDIA MUNICIPAL DE CAUCASIA890906445AntioquiaCaucasiaColombia, Antioquia , CaucasiaTerritorialNo aplica/No perteneceEjecutivoDescentralizadaEn ejecuciónPrestación de serviciosContratación directa2022-10-062022-10-062022-12-30NaTNaTA convenirCédula de CiudadaníaNoNoNoNoNoNoNo56666660566666656666660005666666.060000000NoFuncionamientoDistribuido0ColombianaMujer000566666600NaTNaT
1983017EMPRESAS PÚBLICAS MUNICIPALES DE MALAGA890205049SantanderMálagaColombia, Santander , MálagaTerritorialAmbiente y Desarrollo SostenibleCorporación AutónomaDescentralizadaEn ejecuciónPrestación de serviciosContratación régimen especial2023-06-052023-06-052023-12-28NaTNaTA convenirCédula de CiudadaníaNoNoNoNoNoNoNo12754000018220001093200018220000010932000.0127540000NoFuncionamientoRecursos Propios0COLOMBIANAMujer0000012754000NaTNaT
1100805MUNICIPIO DE MARINILLA890983716AntioquiaMarinillaColombia, Antioquia , MarinillaNacionalServicio PúblicoEjecutivoDescentralizadaEn ejecuciónPrestación de serviciosContratación directa2023-05-052023-05-052023-12-31NaTNaTComo acordado previamenteNo DefinidoNoNoNoNoNoNoNo49966910004996691000049966910.0499669100NoFuncionamientoDistribuido0COLOMBIANOHombre0004996691000NaTNaT
1847297SUBRED INTEGRADA DE SERVICIOS DE SALUD NORTE ESE900971006Distrito Capital de BogotáNo DefinidoColombia, Bogotá, BogotáTerritorialSalud y Protección SocialCorporación AutónomaDescentralizadaEn ejecuciónDecreeLaw092/2017Contratación régimen especial2022-02-172022-02-182023-12-31NaTNaTA convenirCédula de CiudadaníaNoNoNoNoNoNoNo19226262001922626200019226262.0155763259560NoFuncionamientoRecursos Propios0COLOMBIANAMujer0000019226262NaTNaT
Nombre EntidadNit EntidadDepartamentoCiudadLocalizaciónOrdenSectorRamaEntidad CentralizadaEstado ContratoTipo de ContratoModalidad de ContratacionFecha de FirmaFecha de Inicio del ContratoFecha de Fin del ContratoFecha de Inicio de EjecucionFecha de Fin de EjecucionCondiciones de EntregaTipoDocProveedorEs GrupoEs PymeHabilita Pago AdelantadoLiquidaciónObligación AmbientalObligaciones PostconsumoReversionValor del ContratoValor de pago adelantadoValor FacturadoValor Pendiente de PagoValor PagadoValor AmortizadoValor Pendiente de AmortizacionValor Pendiente de EjecucionSaldo CDPSaldo VigenciaEsPostConflictoDestino GastoOrigen de los RecursosDias AdicionadosNacionalidad Representante LegalGénero Representante LegalPresupuesto General de la Nacion – PGNSistema General de ParticipacionesSistema General de RegalíasRecursos Propios (Alcaldías, Gobernaciones y Resguardos Indígenas)Recursos de CreditoRecursos PropiosFecha Inicio LiquidacionFecha Fin Liquidacion
2013134MINISTERIO DE AGRICULTURA Y DESARROLLO RURAL899999028Distrito Capital de BogotáBogotáColombia, Bogotá, BogotáNacionalagriculturaEjecutivoCentralizadaEn ejecuciónPrestación de serviciosContratación directa2022-01-272022-01-282022-09-30NaTNaTNo DefinidoCédula de CiudadaníaNoNoNoNoNoNoNo5693333300569333330005.693333e+07569333330NoInversiónDistribuido0COLOMBIANANo Definido5693333300000NaTNaT
298537INSTITUTO PARA EL DEPORTE Y RECREACION DE YOPAL IDRY800117801CasanareYopalColombia, Casanare , YopalTerritorialdeportesEjecutivoDescentralizadaterminadoPrestación de serviciosContratación directa2022-09-082022-09-082022-12-22NaTNaTA convenirCédula de CiudadaníaNoNoNoNoNoNoNo55999650055999650005.599965e+0655999650NoInversiónDistribuido0COLOMBIANAHombre000559996500NaTNaT
1825282SANTIAGO DE CALI DISTRITO ESPECIAL DEPARTAMENTO ADMINISTRATIVO DE HACIENDA890399011Valle del CaucaCaliColombia, Valle del Cauca , CaliTerritorialHacienda y Crédito PúblicoCorporación AutónomaCentralizadaCerradoPrestación de serviciosContratación directa2022-01-142022-01-212022-06-30NaTNaTNo DefinidoCédula de CiudadaníaNoNoNoNoNoNoNo2113800000211380000002.113800e+0721663660240NoInversiónDistribuido0colombianaNo Definido0002113800000NaTNaT
830352DEPARTAMENTO DEL META8920001488MetaVillavicencioColombia, Meta , VillavicencioTerritorialServicio PúblicoEjecutivoDescentralizadaEn ejecuciónPrestación de serviciosContratación directa2023-05-252023-05-252023-10-29NaTNaTComo acordado previamenteCédula de CiudadaníaNoNoNoNoNoNoNo2464500000246450000002.464500e+07246450000NoInversiónDistribuido0COLOMBIANAMujer0002464500000NaTNaT
184407ALCALDIA DE OCAÑA890501102Norte de SantanderNo DefinidoColombia, Norte de SantanderTerritorialServicio PúblicoEjecutivoCentralizadaterminadoPrestación de serviciosContratación directa2022-08-112022-08-112022-12-27NaTNaTNo DefinidoCédula de CiudadaníaNoNoNoNoNoNoNo11040000011040000011040000000.000000e+00110400000NoInversiónDistribuido0COLOMBIANOMujer0001104000000NaTNaT
296444Secretaría Distrital de Integración Social8999990619Distrito Capital de BogotáNo DefinidoColombia, Bogotá, BogotáTerritorialInclusión Social y ReconciliaciónEjecutivoCentralizadaModificadoPrestación de serviciosContratación directa2022-07-272022-08-012023-02-26NaTNaTComo acordado previamenteCédula de CiudadaníaNoNoNoNoNoNoNo17924500017924500017924500000.000000e+00179245000NoInversiónDistribuido42ColombianaNo Definido0179245000000NaTNaT
1688630MUNICIPIO DE MANIZALES890801053CaldasManizalesColombia, Caldas , ManizalesTerritorialServicio PúblicoEjecutivoCentralizadaEn ejecuciónPrestación de serviciosContratación directa2023-03-162023-03-162023-12-20NaTNaTComo acordado previamenteCédula de CiudadaníaNoNoNoSiNoNoNo2170843900217084390002.170844e+07252245900NoInversiónDistribuido0ColombianoNo Definido0217084390000NaTNaT
1133493ALCALDÍA LOCAL DE SUBA899999061Distrito Capital de BogotáBogotáColombia, Bogotá, BogotáTerritorialServicio PúblicoEjecutivoCentralizadaEn ejecuciónPrestación de serviciosContratación directa2023-05-152023-05-302023-08-29NaTNaTComo acordado previamenteCédula de CiudadaníaNoNoNoNoNoNoNo1200000004133333120000000001.200000e+07120000000NoInversiónRecursos Propios0ColombianoNo Definido0000012000000NaTNaT
1664830REGIONAL DE ASEGURAMIENTO EN SALUD No 1900336524Distrito Capital de BogotáNo DefinidoColombia, Bogotá, BogotáTerritorialdefensaEjecutivoCentralizadaModificadoPrestación de serviciosSelección Abreviada de Menor Cuantía2022-07-272022-08-032023-05-20NaTNaTA convenirNo DefinidoNoSiNoSiNoNoNo10185018840010185018840001.018502e+096790012560NoFuncionamientoDistribuido50COLOMBIANo Definido8490012561695006280000NaTNaT
1368108GOBERNACION DEL MAGDALENA800103920MagdalenaSanta MartaColombia, Magdalena , Santa MartaTerritorialNo aplica/No perteneceEjecutivoCentralizadaSuspendidoObraLicitación pública Obra Publica2022-03-232022-06-132023-04-12NaTNaTNo DefinidoNo DefinidoNoNoNoNoNoNoNo3004220165300300422016530003.004220e+10314361287890NoInversiónDistribuido0COLOMBIANONo Definido0030042201654000NaTNaT